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Prioritized Multiplicative Schwarz Procedures for Solving Linear Systems David Wingate, Nathaniel Powell, Quinn Snell, Kevin Seppi Computer Science Department Brigham Young University Provo, Utah 84602 { wingated, nep8, snell, kseppi}@ cs. byu. edu Abstract We describe a new algorithm designed to quickly and ro-bustly solve general linear problems of the form Ax = b. We describe both serial and parallel versions of the algorithm, which can be considered a prioritized version of an Alter-nating Multiplicative Schwarz procedure. We also adopt a general view of alternating Multiplicative Schwarz proce-dures which motivates their use on arbitrary problems ( even which may not have arisen from problems that are naturally decomposable) by demonstrating that, even in a serial con-text, algorithms should use many, many partitions to ac-celerate convergence; having such an over- partitioned sys-tem also allows easy parallelization of the algorithm, and scales extremely well. We present extensive empirical evi-dence which demonstrates that our algorithm, with a com-panion subsolver, can often improve performance by sev-eral orders of magnitude over the subsolver by itself and over other algorithms. 1. Introduction This work introduces the concept of prioritization ap-plied to Alternating Multiplicative Schwarz ( AMS) proce-dures [ 11] as an effective method for solving problems of the form Ax = b, where A is a large, sparse matrix. AMS procedures are a form of problem decomposition that were originally motivated by trying to solve continu-ous PDEs with irregular domains. The principle is to divide the problem into smaller subproblems ( which are still con-tinuous), then repeatedly sweep over the subproblems, solv-ing each one and updating interface values between prob-lems. These PDEs were not necessarily discretized, and in some cases, the subproblems could be solved directly. The technique was then generalized to discretized PDEs, where a matrix A resulting from a discretization process is bro-ken up into submatrices, each of which correspond directly to some part of the original problem [ 10]. In contrast, we explore the use of AMS- like procedures for arbitrary matrices, regardless of where they originated. In that sense, this work reverses the traditional view of AMS procedures: instead of starting with a known problem and generating a matrix representing its discretization, we be-gin with a matrix, and force a decomposition upon it, even if the underlying problem is not naturally decomposable. This means that the decomposition may or may not corre-spond to anything recognizable in the underlying problem. The results are excellent, in some cases accelerating solu-tion times by many orders of magnitude. The centerpiece of this paper is the development of pri-oritized AMS procedures, in which subdomains are selected according to some priority metric. The principle behind this is the observation that variables with large error strongly in-fluence the solution vector. Corrections of large errors prop-agate additional error backwards through the graph defined by the A matrix. Our algorithm processes variables in prior-ity order by constructing a priority queue using a metric de-rived from the residual. Such prioritized solution methods have shown to provide tremendous speedups in other con-texts [ 9][ 12] for two reasons: first, they focus computation in regions of the problem which are expected to be max-imally productive, and second, they can sometimes avoid processing large parts of the problem. It is well- known that AMS procedures are naturally par-allelizable. However, most researchers using AMS proce-dures as a domain decomposition method split the matrix into a small number of partitions, typically equal to the number of processors. One of the central points of this work is the fact that, even on a single processor, dramatic im-provements are produced if a large number of partitions are used to solve a problem. We demonstrate that in a parallel context, far more partitions than processors should be used. This allows the same performance benefits to be gained on each individual processor, and improves the load- balancing and cache coherency of the algorithm. The goal of this research is to speed up solution times by building on top of existing algorithms. However, our modi- Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium ( IPDPS' 05) 1530- 2075/ 05 $ 20.00 IEEE fied AMS procedures are not accelerators ( in the sense de-fined by Hageman and Young [ 7]), nor can they be con-sidered preconditioners ( as an additive Schwarz procedure can be). No simple linear algebra expression exists which captures the essence of the algorithm or which facilitates easy analysis. For those reasons, we present a more pro-cedural exposition. Since we treat AMS procedures as an algorithmic accelerator for other subsolvers, and not as a preconditioner, we use lambda calculus notation to explic-itly recognize the fact that our algorithm must be used in conjunction with another subsolver. This subsolver may be any solution method, including iterative methods, such as GMRES, CGS, TFQMR, BiCGSTAB, or direct methods, such as more traditional ( I) LU decomposition or Gaussian elimination [ 2]. Thus, we have named the resulting algo-rithm GPS( ?), which is short for General Prioritized Solver, where ? is the companion subsolver. The parallel imple-mentation is called PGPS( ?), which is short for Parallel General Prioritized Solver. Because we wish to think about AMS procedures with-out regard to the underlying problem, we take a perspective akin to partial differentiation on the matrix A: we force arbi-trary subsolvers to solve for a certain set of variables, while holding the rest of the variables constant. We make no as-sumptions about the form of the matrix; for example, we do not assume that the matrix has a diagonal structure ( with any sized bandwidth). Instead of a traditional block decom-position, we adopt more general partitioning notation, and rely on general graph partitioners to decompose the problem and to determine interface edges. Thus, the ideas of over-lap and interface value communication are not treated in the traditional way. However, if a naive partitioning is used ( in which each partition is composed of a contiguous block of variables), and the matrix is already in a strongly diago-nal form, then the result is mathematically equivalent to tra-ditional AMS procedures, which is why we consider our al-gorithm a slight generalization of them. Empirical results demonstrate that the serial version of the final algorithm almost always improves performance for a variety matrix types and sizes, and for a variety of subsolvers. Performance is often improved by several or-ders of magnitude, but for different reasons with different matrix/ solver combinations. Improved performance is pre-served in the parallel version, and excellent parallel scala-bility is demonstrated. 2. The GPS( ?) Algorithm Here, we present the serial version of the GPS( ?) algo-rithm. The next section discusses parallel extensions. The core ideas underlying the GPS( ?) algorithm are quite sim-ple: we partition the variables, we select a subset of vari-ables based on priority, we solve the subset while hold-ing all other variables constant ( using any linear system solver, denoted ?), and then we update any quantities that depended upon variables in the subset. The entire process is repeated until convergence. There are four details that merit attention: 1) How can an arbitrary subsolver be made to solve for some variables, while holding others constant? 2) How should partitions be created? 3) How should parti-tions be selected in order to maximize the efficiency of the algorithm? 4) To what tolerance should the subproblem be solved? The following subsections address each question. A few pieces of notation will be used throughout the pa-per. Let P be a partitioning of variable indices, with each p ? P being a set of variable indices. Let r = Ax- b be the global residual, and rp = Apxp - bp be residual of a sub-problem. . Folding/ Extracting One idea motivating the GPS( ?) al-gorithm is the idea of solving for a subset of all variables, while holding the rest of the variables constant. We term our method folding and extracting, and is an approach com-patible with any sub- solver. The key observation is the fact that if certain variables are held constant, their values may be temporarily folded into the right- hand side vector, and a new sub- problem created. As an example, consider the system Ax = b where A is a 2x2 matrix. Suppose that we wish to solve only for vari-able 0 while holding variable 1 constant. We may expand this system ( using array subscript notation to index vectors and matrices) and, since x[ 1] is constant, rewrite it as where b is the new vector that results from the folding oper-ation. This yields a set of two equations with one unknown. Either may be used to solve for variable 0, although since our estimate for x[ 1] is not guaranteed to be correct, differ-ent equations will result in different answers ( see Section 7 for more discussion on this point). In general, given an n×n matrix A and a partition p, the equation for variable i ? p is: This yields n equations in | p| unknowns, which is an over constrained system. We will select a subset of the equations with which to work; we adopt the convention that we will use the equations corresponding to the variable indices in the partition. We define a selector matrix as Kp[ i, j] = 1 i ? p ? i = j 0 otherwise Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium ( IPDPS' 05) 1530- 2075/ 05 $ 20.00 IEEE Then, to select a subset of equations from A, x and b, we let A = KpAKT p , x = Kpx, and b = Kpb. Note that A is still an n×n matrix, but with many empty rows and columns. If row i in A is empty, column i will also be empty, as will the corresponding entries in both both x and b . The entire system may therefore be compacted by eliminating such zero entries, and mapping variable indices from the original system to new variable indices in the com-pacted system: Ap = compact( A ), xp = compact( x ), and bp = compact( b ). The entire system Apxp = bp may now be passed to an arbitrary subsolver. Other authors define similar reduction procedures in terms of restriction and prolongation operators [ 6][ 10]. We use our notation for simplicity of exposition. Partitioning There are several principles governing the creation of partitions. Partitioning variables is different than a traditional block decomposition on the matrix A, because a partition may contain any subset of variables. Block de-compositions effectively stipulate that variables be parti-tioned contiguously. There are still some constraints on the partitioning. First, partitions must ensure that no sub- problem is under con-strained. This is equivalent to ensuring that every sub-matrix has no empty rows or columns ( unless both are empty), which is equivalent to stipulating that the binary relation defined by the sub- matrix be total and surjective. General graph partitioning algorithms, such as multi- level k- way partitioners [ 8], or k- way partitioners using spectral bisection, do not necessarily generate partitions that satisfy this criterion. However, both conditions are satisfied for any partitioning when the matrix has a non- zero diagonal. In this situation, it is possible to create " naive" partitions where each partition simply contains n/| P| contiguous variables. Although in theory an arbitrary partitioning of the vari-ables could be used, the principle of partitioning is to group related variables together. The idea is that if one variable has a high priority, the related variables in the partition will also have a high priority, and we can solve them all simulta-neously without incurring the overhead of prioritizing each variable individually. A good way to accomplish is to use a partitioning which minimizes the edge cut, which is a com-mon capability of existing k- way partitioning algorithms. Prioritization Once partitions are defined, the next ques-tion is determining the order in which partitions should be processed. There are two options: first, a naive sequen-tial method may be used. Although simple, this method is surprisingly effective for well- ordered matrices, as demon-strated in Section 6. A second option is to process partitions in priority order. For many of the experiments, we therefore define a pri-ority queue which orders partitions. The queue has a pri-ority metric associated with it, which we denote H( p). For reasons that will be explained shortly, we use the 2- norm-squared of the residual vector of each sub- problem as the priority of that partition: H( p) . In princi-ple, many different priority metrics could be used, but this is left to future research. For the purposes of the algorithm, the priority queue must support three operations: insert, pop, and reprioritize. The third operation is necessary be-cause the priority of a partition will often change when it is deep in the queue, as other partitions, upon which it de-pends, are solved. Subproblem tolerance The final issue involves determin-ing the tolerance to which subproblems should be solved. There are three possibilities: specify a maximum number of iterations, specify a subproblem tolerance, or both. Specifying a subproblem tolerance merits some discus-sion. Assume that we wish to solve Ax = b to tolerance of using a 2- norm measure on the residual vector. Clearly, it is not sufficient to solve each subproblem to within , be-cause the combination of the residual norms of all of the subproblems may yield an overall residual norm which is greater than the global tolerance. Instead, it is necessary to allocate error to different sub-problems. If we assume that error is allocated uniformly, for instance, then , and by the uniformity assumption, (| P| ?p) 1/ 2 < , meaning that ? 1/ 2 p < | P| 1/ 2 . This states that if error is allocated uniformly, the subprob-lem tolerance must be less than to ensure that the global tolerance will be achieved. However, this is only one way of allocating error to par-titions. Other methods, including methods which dynami-cally allocate error as the algorithm progresses, are left to future research. In our algorithm, we specify both a subdo-main tolerance, as well as a maximum number of subdo-main iterations. 2.1. Algorithm Details Here, we present the final algorithm which incorporates the issues discussed in this section. We will briefly discuss an optimization related to incremental error computations, and then present the final algorithm. Incremental error computations As previously noted, many different stopping criteria could be used with the GPS( ?) algorithm. We have presented the most common, which is the 2- norm of the residual vector ( this is also the criteria that was used in all of our experiments). The exten-sion to other stopping criteria is straightforward, but messy, and has been omitted. An advantage of the 2- norm of the Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium ( IPDPS' 05) 1530- 2075/ 05 $ 20.00 IEEE Algorithm 1 GPS( ?) Initialization 1: r := b - Ax 8: PQ. insert( p) Main Loop 1: repeat 2: // Solve the highest priority partition 3: p := PQ. pop() 4: ( xp, bp) := fold( p, A, x, b) 5: xp := ?( Ap, xp, bp) 6: ( x, r) := extract( p, x, xp, r, rp) 7: rp := bp - Apxp 8: 9: // Update the global residual 21: ?G := ?G + r[ i] 2 22: 23: // Reprioritize partitions 24: for all p ? PartDep( p) do 25: PQ. reprioritize( p ) 26: until ?G < residual vector is that efficient incremental computation can be derived, which is compatible with the fact that, due to the prioritization aspects of GPS( ?), variables do not always need to be processed. Practically, squaring and square root operations can be avoided if the 2- norm squared is com-pared to the tolerance squared; our algorithm employs this. We use this technique for computing global error estimates, as well as maintaining error estimates for the sub- problems. Empirically, the technique is stable, but suffers from round-off errors, which is remedied by periodically performing a full residual norm calculation ( not shown in Algorithm 1). Full algorithm Algorithm 1 shows the full pseudocode for the serial GPS( ?) algorithm. Here, VarDep( p) returns Algorithm 2 fold( p, A, x, b) 1: for all i ? p do 5: return x, r the variable dependents of a partition p, which is the set of all variables with an edge to any variable in p. Simi-larly, PartDep( p) returns the partition dependents of a par-tition p, which is the set of all partitions which contain at least one variable in VarDep( p). The gv to lv function (" global- variable- to- local- variable") maps variable indices from A to submatrix indices in Ap, and the var to part function maps variables to their owning partitions. 3. Parallel Implementation The enhancements of partitioning and prioritization nat-urally facilitate an efficient parallel implementation. This section discusses one way in which the GPS( ?) algorithm can be parallelized, and discusses some of additional issues encountered when moving to a parallel architecture. In our algorithm, each processor owns ( or is responsible for) a set of partitions. These partitions are termed " local" partitions, which contain " local" variables. All other parti-tions and variables are " foreign." In a corresponding gener-alization of notation, we will use ?Pr to denote the sum of all ?p for p local to processor Pr. To create PGPS( ?), we adopted an architecture with some asynchronous aspects as well as some synchronous aspects. The basic algorithm is substantially the same as the serial algorithm, except for two major changes which merit detailed discussion. First, since partitions are assigned to processors, there is the issue of communicating the values of variables between processors. Secondly, there is the is-sue of termination. 3.1. Variable Communication The PGPS( ?) algorithm embodies the same core con-cepts as the serial version, except that the data and com-putation has been distributed. First, each processor selects Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium ( IPDPS' 05) 1530- 2075/ 05 $ 20.00 IEEE the local partition p with the highest priority and solves it. Then, the priorities and residuals of any other local parti-tions which depend on p are recomputed. Now, however, PGPS( ?) must execute steps that GPS( ?) did not need to: it must communicate the values in p to all other processors which depend upon p, and it must receive and process up-dates from other processors. The values in a partition do not necessarily need to be communicated to every other processor. In fact, the proces-sors which need the new values for partition p are partitions which have some local variable that depends upon a value from a variable in p. We term this set ProcDep( p), and is typically very small in sparse matrices. Communicating new values can be accomplished with a single message sent to each processor in ProcDep( p). To avoid starvation of processors, a processor receives a maxi-mum of n messages, where n is the number of processors. Receiving an update message from a foreign processor is conceptually the same as the solving a local subprob-lem associated with a partition p. The various quantities that depend on variables within p must be recomputed: parti-tion priorities, partition error estimates, the global error es-timate, and variable residuals. 3.2. Termination Detection Since a processor is only responsible for a subset of the variables, it can only compute a subset of the overall error. Each processor's error estimate must be combined to form a global error estimate, which can be used to determine when to terminate. Although there are many ways to compute such a global error estimate, an effective way is a periodic reduction. In our implementation, all processors reduce the local ?Pr er-ror values every 100 iterations. The number of iterations between synchronizations is a tunable parameter. Theoreti-cally, if it is set too high, the algorithm performs unneces-sary work, and if it is set too low, there can be significant overhead. Empirically, however, it had very little impact on the solution times. 3.3. Discussion There are several advantages of this quasi- synchronous architecture. First, since processors communicate changed values only to the processors that need it, and since ProcDep( p) is typically very different for different parti-tions, processors can work at their own rate. In addition, the overall volume of communication is fairly small, since par-titions are usually selected to contain no more than about 100 variables. Naturally, all master/ slave bottle-necks are avoided. The primary disadvantage is that many small messages are generated, which can incur high latency penalties. In addition, the synchronous computation of error means that processors cannot work completely at their own rate. This creates some inefficiencies that could be remedied with a more sophisticated termination detection. 3.4. Full Algorithm Algorithm 4 shows the full PGPS( ?) algorithm. Note that processors now operate only on local partitions. The PQ contains only local partitions, and whenever a partition changes ( either as a result of popping off the local PQ and solving, or as a result of a message from a foreign proces-sor), only local errors and residuals are recomputed. Here, LocalVarDep( p) returns the local variable de-pendents of a partition p, which is the same as VarDep( p), except that it contains only variables local to Pr. LocalPartDep( p) returns the local partition depen-dents of a partition p, and only contains local partitions. The ProcDep( p) function returns a set of processor de-pendents of a partition p, which is the set of all processors that own one variable which depends upon one vari-able in p. Lines 2- 5 show pseudocode for the simple synchronous termination test. Lines 18- 38 constitute the bulk of the dif-ference between PGPS( ?) and GPS( ?), but is conceptually simple: each partition update message received triggers pro-cessing that is the same as the processing which happens which a local partition changes. 4. Algorithm Design Details There are many issues surrounding the design of an algo-rithm such as GPS( ?). This section briefly examines some of the more theoretical aspects, but we stress that space pre-cludes a full analysis. Here, we only briefly consider con-vergence and miscellaneous design details. 4.1. Convergence Technically, the introduction of prioritization into the so-lution process moves the GPS( ?) algorithm into the class of chaotic ( or asynchronous) relaxation algorithms. Proofs of convergence of such algorithms have traditionally imposed strict requirements on the problems to be solved; a typical condition is that ?( A) < 1 [ 3][ 5][ 4]. In principle, the convergence of GPS( ?) should there-fore be governed by two factors: first, A must satisfy any constraints that the subsolver requires. If CGS is selected as a subsolver, for instance, then A must be symmetric posi-tive definite. Second, it seems that A should satisfy any ad-ditional constraints that asynchronous relaxations impose. Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium ( IPDPS' 05) 1530- 2075/ 05 $ 20.00 IEEE Algorithm 4 PGPS( ?) Main Loop 1: repeat 2: // Synchronously compute global error 3: if time to resync then 4: ensure all messages have been received 5: ?G = Reduce( ?Pr) 6: 7: // Solve the highest priority partition 8: // and update the global residual 9: same as serial 10: 11: // Update partition and variable residuals 12: // and reprioritize partitions 13: for all i ? LocalVarDep( p) do 14: same as serial 15: for all p ? LocalPartDep( p) do 16: same as serial 17: 18: // Send new partition values 19: for all i ? ProcDep( p) do 20: Send( i, p, xp) 21: 22: // Receive new values 23: for i = 1 to nproc do 24: if no messages waiting then 25: break Empirically however, the GPS( ?) algorithm is unique in that neither constraint appears to be fully in force. In our ex-periments, GPS( ?) robustly converged for almost all matri-ces tested ( assuming that preconditioner succeeded on the sub- problems), each of which was non- convergent ( that is, ?( A) = 1). In addition, at least in theory, the GPS( ?) algo-rithm actually relaxes some of the constraints imposed on solvers and matrices. The issues of convergence merit de-tailed attention, so they are largely left for future research. However, the idea that GPS( ?) relaxes some requirements merits an example. Consider the matrix A = ? ? ? ? 3 2 1 0 2 3 0 4 0 0 3 2 0 0 2 3 ? ? ? ? and a right- hand side of b = [ 1, 1, 1, 1] T . Although A is positive definite, it is not symmetric. A solver that requires that the matrix be SPD, such as CG, diverges when attempt-ing to solve Ax = b. However, if we define two partitions p1 = { 1, 2} and p2 = { 3, 4}, then A1 = A2 = 3 2 2 3 both of which are positive definite. CG can therefore solve both sub- problems, meaning that GPS( CG) can be used to solve the entire system. The rate of convergence of GPS( ?) is unknown. Since it is technically a chaotic relaxation algorithm, all known con-vergence proofs simply state that if certain conditions re-lating to the matrix are met, and all variables are relaxed an infinite number of times, the system will converge. No guaranteed rates are known except in very specific circum-stances, but empirically, we have observed excellent rates. Additional material and analysis on convergence of AMS procedures can be found in [ 6]. 4.2. Miscellaneous Details Currently, determining when to stop the subsolver is largely heuristic. As noted, GPS( ?) uses a combination of a subproblem tolerance and a small maximum number of subproblem iterations. Although sufficient for convergence, both methods leave something to be desired, because both numbers are static. Some initial experimentation has indi-cated that dynamically changing the subproblem tolerance ( or the maximum number of subdomain iterations) can yield improved performance. This is consistent with basic princi-ples: it is good to work quickly across many different parts of a problem, to quickly reduce large errors. However, as time progresses, it becomes more important to solve sub-problems to a higher accuracy. 5. Experimental Setup A number of experiments were run to isolate and quan-tify all relevant behaviors of the GPS( ?) and PGPS( ?) algo-rithms. The bulk of the experiments were designed to quan-tify the benefits of different enhancements, and across sev-eral different subsolvers. It examined 1) the folding / ex-tracting technique; 2) the benefits of prioritization; and 3) the efficiency of the parallel version of the code. Experiments were run across a wide variety of matrices, which are described in Figure 1. An effort was made to se-lect matrices from different discplines, with different sizes, Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium ( IPDPS' 05) 1530- 2075/ 05 $ 20.00 IEEE Matrix name Description Size NNZ RHS SPD bcsstk08 BCS – TV studio 1,074 7,017 ( made) Yes e20r0000 2D fluid flow in a driven cavity 4,241 131,556 MM No e40r5000 2D fluid flow in a driven cavity 17,281 553,956 MM No fidap003 Matrix from the FIDAP package 1,821 52,659 MM No fidap006 Matrix from the FIDAP package 1,651 49,479 MM No fidap010 Matrix from the FIDAP package 2,410 54,816 MM No fidap011 Matrix from the FIDAP package 16,614 1,091,362 MM No fidap014 Matrix from the FIDAP package 3,251 66,647 MM No fidap035 Matrix from the FIDAP package 19,716 218,308 MM No fidapm33 Matrix from the FIDAP package 2,353 23,765 MM No fs 760 3 Mixed kinetics diffusion problem 760 5,976 ( made) No hp 400 5 Thermal convection; 5- pt stencil 160,000 798,400 ( in- house) No mcar- 160000 Reinforcement learning problem 160,000 637,831 ( in- house) No nnc1374 Advanced gas- cooled nuclear reactor core 1,374 8,606 ( made) No rdb2048l 2D reaction- diffusion model 2,048 12,032 ( made) No sap- 160000 Reinforcement learning problem 160,000 639,996 ( in- house) No s3dkt3m2 FEA of cylindrical shell 90,449 1,921,955 ( made) Yes sherman2 Thermal simulation with steam injection 1,080 23,094 MM No sherman3 IMPES simulation of a black oil model 5,005 20,033 MM No Figure 1. Descriptions of the matrices used. and with different numerical properties. Most of the matri-ces used can be found at NIST's MatrixMarket. 1 Excep-tions are labeled with an " in- house" label in the RHS col-umn of Figure 1. A fair comparison of GPS( ?) to other solvers is not easy. We selected unpreconditionedGMRES as the general base-line, because of its popularity and robustness across many different matrix types. As we will show, GPS( ?) often out-performs GMRES. On the one hand, it is not surprising that GPS( ?) outperforms an unpreconditioned solver, but on the other hand, GPS( ?) is not preconditioned either ( this is be-cause it is theoretically possible to develop a preconditioned version of GPS( ?) which operates on a preconditioned ma-trix, instead of directly on the matrix A). The most natural direct competitor is GMRES, preconditioned with an Ad-ditive Schwarz Method, and using a standard ILU subdo-main preconditioner. With these issues in mind, most of the experiments were run using GMRES ( labeled as " P-GMRES"), GMRES with an ASM+ ILU preconditioner ( la-beled as " P- GMRES w/ ASM+ ILU"), GPS( ?) ( labeled as " GPS- PQ" or " GPS- SEQ"), and GMRES with a standard MAT preconditioner ( labeled as " P- GMRES w/ MAT"). For most of the experiments, the parameters for GPS( ?) ( such as number of partitions, maximum number of sub-domain iterations, etc.) were aggressively hand- tuned to yield the best performance. When solving the problems, tol-erances were selected primarily to elicit interesting behav- 1 http:// math. nist. gov/ MatrixMarket/ ior. For GPS( ?), when solving to a specified tolerance, sub-problem tolerances were always set to be | P|. For all of the experiments the subsolvers incorporated into the GPS( ?) code were taken from the " Portable, Ex-tensible Toolkit for Scientific Computation" ( PETSc) li-brary [ 1]. Experimentation with Sandia National Labora-tory's " Aztec" package yielded worse performance, and did not have the preconditioners needed, so it was dropped. To help isolate the benefits of the fold/ extract approach from the benefits of prioritization, we will also reference an algorithm named " GPS- SEQ." This is a variant of the GPS( ?) algorithm which selects partitions sequentially, in-stead of in priority order. If clarification is needed, we will reference the prioritized version of GPS( ?) as " GPS- PQ." Many of the matrices from the MatrixMarket have a RHS that is also downloadable, but some do not. For those that do not, one was manufactured by selecting a random x vec-tor with each x[ i] ? ( 0, 1), then multiplying it through A to generate b. These are indicated in Figure 1 with "( made)" in the RHS column. Regardless of where the RHS came from, an initial guess of x = 0 was always used. Both the GPS( ?) and PGPS( ?) depend on having a par-tition of the variables. Since almost all of the matrices are strongly diagonal, a naive partitioning was used. Each par-tition was assigned n/| P| contiguous variables. Serial results were obtained on a dual processor 2.4GHz P4 machine with 2G of RAM. Parallel results were obtained on a fully connected cluster of dual processor 2.4GHz P4 machines, each with 2G of RAM and equipped with Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium ( IPDPS' 05) 1530- 2075/ 05 $ 20.00 IEEE Myrinet interconnects. All code was written in C with MPI. 6. Results The results of our experiments were very positive. We begin by presenting the general serial results, followed by the parallel scalability results. To be fair, we point out cases in which the algorithm fails, or yields worse performance than a baseline. The primary criterion measuring success is the wall- clock time needed to reduce the ( unpreconditioned) 2- norm of the residual to a specific tolerance. 6.1. Serial results Figure 2, top three rows, shows a representative sample of results for the GPS( ?) algorithm on a variety of matri-ces. The story is complicated: the various algorithms out-perform each other on different problems, and even on the same problem, different algorithms may win or lose based on what tolerance was selected. For this reason, we opted to present the majority of results in graphical form. However, several general patterns are clearly evident, and the winner on any given matrix is usually clear in the limit. There are three broad types of result: • GPS( ?) dramatically improves performance ( by up to several orders of magnitude) [ bcsstk08, e20r0000, fi-dap006, fidap014, sap- 160000, sherman2] • GPS( ?) barely affects performance [ e40r5000, fi-dap011, fs 760 3, mcar- 160000] • GPS( ?) dramatically worsens performance ( some-times making the problem impossible to solve) [ fi-dap003, fidap010, nnc1374, fidap035, fidapm33, hp 400 5, s3dkt3m2, rdb2048l, sherman3] Themost exciting results in our experiments indicate that GPS( ?) yielded excellent performance on a wide variety of matrices. On the bcsstk08 and sap- 160000 problems ( see Figure 2), GPS( ?) solves it instantly while other algorithms gradually reduce the residual norm. For the e20r0000 ( Fig-ure 2) and fidap014 problems, GPS( ?) reliably reduces the residual norm better than all other solvers. On the fidap006 problem, GPS( ?) bottoms out at a residual norm of 0.118 after about 3 seconds, while P- GMRES achieves a residual norm of 0.135 after 40 seconds. Of course, not every posi-tive result is as compelling: on the sherman2 matrix, for ex-ample, GPS( ?) can outperform P- GMRES by two orders of magnitude, but only with a small number of partitions. Naturally, GPS( ?) is not a panacea. For some prob-lems ( such as e40r5000), no algorithm worked. On the fs 760 3 problem, GPS( ?) stabilizes at a residual norm lower than that of P- GMRES, but GPS- SEQ achieves the lowest residual. The fidap011 problem was an interesting case: P- GMRES and GPS- PQ flip- flopped twice, although GPS- PQ eventually won out. For themcar- 160000 problem, GPS- PQ almost instantly solves it, but the GPS- SEQ also solves it almost instantly, and to a better tolerance. This case illustrates two important points: first, since GPS- PQ and GPS- SEQ performed about the same, it appears that prior-itization is not the defining performance factor, but the fact that a multiplicative Schwarz procedure was being used. Second, it is clear that for a problem like this, a hybrid al-gorithm, in which some iterations of GPS( ?) are run until stabilization, followed by some iterations of another solver, would probably best either algorithm alone. We also observed situations where GPS( ?) degraded performance substantially. Often, it was the case that al-though GPS( ?) could reduce the tolerance reliably, other algorithms managed to solve a problem instantly. In other cases, GPS( ?) drives down residual norm down quickly and reliably, but another algorithm achieves a lower norm even more quickly. The fidap003, fidap010, nnc1374 and fi-dap035 problems all showed an interesting general pattern: P- GMRES was able to reduce the residual norm quickly, but GPS( ?) could not. If the number of partitions used was ad-justed downward, however, GPS( ?) approached closer and closer to the performance of P- GMRES. 6.2. Parallel Results Figure 2 ( bottow two rows) show representative paral-lel results for PGPS( ?). The first two graphs show the wall-clock time of P- GMRES, P- GMRES/ ASM+ ILU, PGPS- PQ and PGPS- SEQ, while the second two graphs show the speedup of the same algorithms. The parallel results are pre-sented for the s3dkt3m2 and hp 400 5 matrices. Here, we are benchmarking two distinct parallel imple-mentations: the P- GMRES and P- GMRES/ ASM+ ILU algo-rithms are the standard PETSc implementations, using their MPI- based parallelization. Recall that although PGPS( ?) uses PETSc as a subsolver, it is not a parallel subsolver. The parallel code for PGPS( ?) was developed in- house. The interpretation of these results requires some care. Specifically, the results of different algorithms should not be compared to each other. This is because the point of these figures is to show the parallel scalability and speedup of each algorithm on two different matrices. To demonstrate this in a clear way, we used different tolerances for differ-ent algorithms, meaning that each algorithm is effectively solving a different problem. The results demonstrate excellent overall scalabil-ity. PGPS( ?) consistently scales almost linearly with the number of processors, and although some parallel over-head was seen, it did not appear to be as pronounced as some of the overhead that the PETSc library in-curred. This is also an important validation of our assertion that cross- processor communication overhead is not pro- Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium ( IPDPS' 05) 1530- 2075/ 05 $ 20.00 IEEE hibitive in PGPS( ?), even with the quasi- synchronous architecture we adopted. The speedup graphs indicate that, for both PGPS( ?) and PETSc, there is room for improvement. Neither pack-age bests the other consistently, although GPS- SEQ always shows the best speedup. Of course, experiments on more than two matrices would need to be run to truly determine how the packages compare. The competition with PETSc is significant not only be-cause PGPS( ?) appears to scale better ( demonstrat-ing that an un- optimized prototype code is similar to a well- developed library relied on by many researchers), but also because it was easier to implement. 6.3. Miscellaneous Results One meta result that does not fall neatly into any cat-egory is the fact that throughout our experimentation, very few cases of divergence were observed while using GPS( ?), assuming that the preconditioner suceeded on the subprob-lems. Considering the highly asynchronous nature of the al-gorithm, and the discussion of convergence in Section 4.1, this result is surprising, and merits further study. Finally, we note that tuning the parameters of the algorithm is diffi-cult. For example, performance varies significantly as func-tion of the number of partitions used, which is another issue for future research. 7. Conclusions and Future Research Perhaps the best way to describe our final conclusions is this: initial experimentation using GPS( ?) and PGPS( ?) generated many promising results, and indicates significant potential for the idea of prioritization. Empirically, we have shown that even by themselves, the AMS procedures em-bodied in GPS( ?) and PGPS( ?) can provide dramatic per-formance benefits for many problems, regardless of the ori-gin of the underlying problem. The addition of prioritiza-tion to these procedures can often further improve perfor-mance. Of course, there is no reason to suppose that priori-tizing subdomains in an AMS procedure is the only way that prioritization can be integrated into an algorithm. Studying other ways to accomplish this ( perhaps by deriving directly prioritized versions of algorithms) is a significant direction for future research. We have also shown that GPS( ?) is ef-fectively parallelizable, allowing many different subsolvers to be used in a general parallel framework. The GPS( ?) algorithm is not perfect, of course. Use of the GPS( ?) sometimes worsened performance over baseline algorithms, and sometimes lead to divergence ( especially if the partitioning was not done carefully). Perhaps this re-sult simply means that GPS( ?) fits squarely with other lin-ear system solvers: no single algorithm always outperforms every other algorithm, and very few algorithms are guaran-teed to converge on every problem. Overall, our results indicate that AMS procedures can be effectively used to accelerate many problems. This is true even in the serial case, and even for matrices which came from an unknown underlying problem. Perhaps more gen-erally, the results indicate that there is still room for im-provements to the current generation of best solvers. This motivates continued research into advances such as prioriti-zation with partitioning, in an effort to one day find the op-timal way of solving large sparse linear systems. Acknowledgements David Wingate is supported under a National Science Foundation Graduate Research Fellowship. References [ 1] S. Balay, K. Buschelman, V. Eijkhout, W. D. Gropp, D. Kaushik, M. G. Knepley, L. C. McInnes, B. F. Smith, and H. Zhang. PETSc users manual. Technical Report ANL- 95/ 11 - Revision 2.1.5, Argonne National Laboratory, 2004. [ 2] R. Barrett, M. Berry, T. F. Chan, J. Demmel, J. Donato, J. Dongarra, V. Eijkhout, R. Pozo, C. Romine, and H. V. der Vorst. Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, 2nd Edition. SIAM, Philadel-phia, PA, 1994. [ 3] D. P. Bertsekas and J. N. Tsitsiklis. Parallel and Distributed Computation: Numerical Methods. Prentice- Hall, Engle-wood Cliffs, NJ, 1989. [ 4] D. Chazan and W. Miranker. Chaotic relaxation. In Lin-ear Algebra and its Applications, volume 2, pages 199– 222, 1969. [ 5] V. Gullapalli and A. G. Barto. Convergence of indirect adap-tive asynchronous value iteration algorithms. In Advances in Neural Information Processing Systems, volume 6, pages 695– 702, 1994. [ 6] W. Hackbusch. Iterative Solution of Large Sparse Systems of Equations. Springer- Verlag, New York, 1994. [ 7] L. A. Hageman and D. M. Young. Applied Iterative Meth-ods. Academic Press, New York, NY, 1981. [ 8] G. Karypis and V. Kumar. Multilevel k- way partitioning scheme for irregular graphs. Journal of Parallel and Dis-tributed Computing, 48( 1): 96– 129, 1998. [ 9] A. W. Moore and C. G. Atkeson. Prioritized sweeping: Re-inforcement learning with less data and less time. Machine Learning, 13: 103– 130, 1993. [ 10] Y. Saad. Iterative Methods for Sparse Linear Systems. PWS Publishing, Boston, 1996. [ 11] H. A. Schwarz. Gesammelte Mathematische Abhandlungen, volume 2. Springer- Verlag, 1890. [ 12] D. Wingate and K. Seppi. P3VI: A partitioned, prioritized, parallel value iterator. In Proceedings of the Twenty- First International Conference on Machine Learning, pages 863– 870, 2004. Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium ( IPDPS' 05) 1530- 2075/ 05 $ 20.00 IEEE 1e- 05 1e- 04 0.001 0.01 0.1 1 10 100 0 4 8 12 16 20 24 28 32 36 40 Residual norm Time ( seconds) P- GMRES P- GMRES w/ MAT P- GMRES w/ ASM+ ILU GPS- SEQ GPS- PQ 1e- 09 1e- 08 1e- 07 1e- 06 1e- 05 1e- 04 0.001 0.01 0.1 1 0 4 8 12 16 20 24 28 32 36 40 Residual norm Time ( seconds) 10000 100000 1e+ 06 1e+ 07 0 20 40 60 80 100 Residual norm Time ( seconds) 1e- 08 1e- 07 1e- 06 1e- 05 1e- 04 0.001 0.01 0.1 1 10 0 4 8 12 16 20 24 28 32 36 40 Residual norm Time ( seconds) 1e- 08 1e- 06 1e- 04 0.01 1 100 10000 1e+ 06 1e+ 08 1e+ 10 1e+ 12 0 4 8 12 16 20 24 28 32 36 40 Residual norm Time ( seconds) 1e- 08 1e- 06 1e- 04 0.01 1 100 10000 1e+ 06 0 4 8 12 16 20 24 28 32 36 40 Residual norm Time ( seconds) 0.1 1 10 100 1000 1 2 4 8 16 32 64 Time ( seconds) Number of processors 0.1 1 10 100 1 2 4 8 16 32 64 Time ( seconds) Number of processors 0 5 10 15 20 25 30 35 40 1 2 4 8 16 32 64 Speedup Number of processors 0 5 10 15 20 25 30 35 40 1 2 4 8 16 32 64 Speedup Number of processors Figure 2. Performance results ( top three rows). The matrices shown are ( left to right, top to bot-tom) e20r0000, SAP- 160000, fidap011, MCAR- 160000, bcsstk08, and s3dkt3m2. Parallel performance results ( fourth row) and speedup results ( bottom row) for the s3dkt3m2 ( left) and hp 400 5 ( right) ma-trices. The same legend ( shown in the upper- left graph) is used for all graphs. Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium ( IPDPS' 05) 1530- 2075/ 05 $ 20.00 IEEE
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Ole Miss wraps up its three-game homestand with the Alabama Crimson Tide on Wednesday.
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Make ceratin to use a good quality apple pie filling, I use E.D.Smith brand for this, it really makes a difference in the taste. This makes two strudels, you can cut the recipe in half and use just one puff pastry sheet and one can pie filling. This takes much less time to make than your traditional strudel version, but I think it is just as good! Prep time includes 40 minutes pastry thawing time.
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Hey I'm in for windsong. Today being monday is iffy for me, but I should be free the rest of the week and have a flexible schedule to work with, so when do you want to battle? Also I'm gmt -6 (-5 with DST currently)
I'd appreciate an effort, this is like the worst time possible for me. I can play at 12:15 am your time, i'm pretty sure that you can wait 2 hours on a Friday night
It wasn't that late, it was midnight your time :v About the time you gave me, if you mean 10pm your time on Friday I can't cause i'm working from 10pm to midnight your time. So once again I could play at midnight this day but not sooner. But if you meant 10am your time, then i could as it means midnight my time.
enjoy ^^ http://pokemon.aesoft.org/replay-DIDIER-DROGBAAAAA-vs-shinycarletto--08-giugno-2012-at-20h35-1
iirc it survives Power Whip from Ferrothorn 100% of the time. Actually I'll be honest with you, those EV's are not mine, I just used them because someone in my team had already used it, he said it survives Power Whip from Ferrothorn and it was such a great partner to Heracross. So honestly, if you really want to try other EV combinations go ahead. Iirc making your subs survive Gyro Ball was not worth it. But making it survive 100% of the time against Power Whip sure does a lot to him. Ferrothorn is a smaller problem, as you have Focus Blast, but using him as setup fodder is great. If you really want to, Blue Star was the one using it, you can ask him if there is any other reason for those EVs...
Sorry for taking so long... I saw it just now. And of course I don't mind giving it to you. Get Free (Thundurus) (M) @ Leftovers Trait: Prankster EVs: 208 HP / 48 Def / 252 Spd Timid Nature (+Spd, -Atk) - Nasty Plot - Thunderbolt - Focus Blast - Substitute I hope it helps you...
Heard what happened.....I'm really sorry to hear that and I hope that everything works out for the best. We're gonna miss having you around for the threepeat quest, but I promise we're gonna do our best to win it for you and dedicate the whole tournament to you and your dad! Hopefully I still catch you around now and again man, keep me in the loop :)
Comment on my warstory nig, 5th Gen! Also thanks for reminding me about Wobb not being banned, I'm gonna use it to top the ladder again ^_^


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On Thursday, over 70 members of the American Dream Movement, led by MoveOn.org, formed an unemployment line, waved signs to motorists passing by, and chanted outside of U.S. Representative Heath Shuler's Asheville office to demand that Washington make Wall Street and millionaires pay their fair share to create jobs, rather than make cuts that hit the middle class and poor. The event, called "Line Up for Jobs," was part of a national week of action organized in solidarity with Occupy Wall Street and is part of an escalation of American Dream Movement events that have been taking place all summer.
For months, hundreds of thousands of members of the American Dream Movement have been urging Washington to focus on creating jobs and make the tax system work for all Americans, not just the super-rich. Yet there are still politicians in Washington who would rather force deep cuts to important programs that the middle class depends on just so they can give tax breaks to the rich—all the while doing nothing to create jobs.
"The politicians in Washington are completely out of touch with the struggles Americans are going through," said Stephanie Hellert, a local MoveOn member. "It's as if they didn't know there was a jobs crisis happening in America. Instead of creating jobs, Washington has been focused on budget cuts and gutting vital programs the middle class depends on."
"We're sick of politicians in Washington putting the big banks and corporations before working families," said Hellert. "We need an economy that works for all Americans, not just the richest one percent. Americans need jobs, not cuts, and we should pay for them by making millionaires and corporations pay their fair share in taxes just like the rest of us do."
The event in Asheville was organized by the new American Dream Movement. The American Dream Movement is a grassroots effort inspired by protests in Wisconsin and fueled by the brutal right-wing attacks on the middle class and the poor. The American Dream Movement is made up of millions of ordinary Americans, along with over 80 leading grassroots organizations, fighting together for good jobs for all Americans, securing a future for our children and our communities, and finally making corporations and the super-rich pay their fair share in taxes.
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Purple Paw Dashboard Caddy
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A collection of news and information related to Peter Boulware published by this site and its partners.
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Game 15: Ravens find way in desert
Game 17: Road warriors
Story | Jan 14, 2002In a postseason defined by home-turf dominance, the Ravens raided Pro Player Stadium in a familiar, battle-tested vehicle. Unleashing a smash-mouth running game to complement a menacing defense, the Ravens notched the first road win of these playoffs... -
Game 1: Fresh start sours in hurry
Story | Sep 13, 1999The new era of Ravens football looked painfully similar to the old one. Quarterback Scott Mitchell was as ineffective as Jim Harbaugh. The offensive line was inept, but yesterday there was no Wally Williams or Orlando Brown to blame. An inexperienced... -
Game 3: Browns-bag lunch delicious to Ravens
Story | Sep 27, 1999The Ravens didn't care that they had only 150 passing yards against the next-to-worst defense in the league or that their quarterback of the future looked a little like their quarterback of the past two games with three interceptions. And they weren't... -
Game 4: Ravens catch spirit, bombs
Story | Oct 4, 1999The Ravens displayed more desire and passion in one game yesterday than they did in the previous three seasons. First, running back Errict Rhett talked trash and accidentally screamed into the official's microphone on the field that he was "running... -
Game 6: Chiefs slow Case attack to a crawl
Story | Oct 22, 1999The honeymoon period for first-year Ravens coach Brian Billick and quarterback Stoney Case began coming to an end last night. Billick's offensive unit was booed periodically throughout the game as a sellout crowd of 68,771 and a national television... -
Game 15: Bittersweet win whets appetite
Story | Dec 27, 1999In less than a 30-minute span, the Ravens were happy, disappointed and hopeful as they discussed their past, present and future. Shortly after a 22-0 win yesterday against the Cincinnati Bengals in the team's first-ever shutout, the Ravens displayed... -
Game 3: Jaguars pass by Ravens, 24-10
Story | Sep 21, 1998There was no suspense in Jaguars- Ravens V. The previous four games were decided by a combined nine points, but this time Jacksonville scored two third-quarter touchdowns and finished with 519 yards of total offense to easily defeat the Ravens, 24-10,... -
Game 5: Ravens stumble again trying to take next step
Story | Oct 12, 1998As Ravens offensive linemen Wally Williams and Jonathan Ogden sat next to each other on stools in front of their lockers, they began to think about the team's three-year history in Baltimore. It has been an emotional ride with a familiar pattern. Win,... -
Game 6: For Ravens, it gets uglier
Story | Oct 19, 1998The Ravens don't have a quarterback controversy, they have a quarterback problem. They have one of the NFL's highest-paid offensive lines, yet one of the most unproductive. They've tried countless offensive formations, yet nothing works.On a day when... -
Game 9: Ravens' wait ends in a smile
Story | Nov 9, 1998The referees were huddled in the end zone after time ran out, trying to determine if there was a penalty that would give the Oakland Raiders another shot to tie or win the game yesterday, and the Ravens were getting some bad vibes.Ravens center Wally...


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Finished in 1947 and lost to readers until now, House of Earth is legendary folk singer and American icon Woody Guthrie�s only finished novel. A powerful portrait of Dust Bowl America, it�s the story of an ordinary couple�s dreams of a bett......more
Finished in 1947 and lost to readers until now, House of Earth is legendary folk singer and American icon Woody Guthrie�s only finished novel. A powerful portrait of Dust Bowl America, it�s the story of an ordinary couple�s dreams of a better life and their search for love and meaning in a corrupt world.
Tike and Ella May Hamlin are struggling to plant roots in the arid land of the Texas panhandle. The husband and wife live in a precarious wooden farm shack, but Tike yearns for a sturdy house that will protect them from the treacherous elements. Thanks to a five-cent government pamphlet, Tike has the know-how to build a simple adobe dwelling, a structure made from the land itself�fireproof, windproof, Dust Bowl-proof. A house of earth.
A story of rural realism and progressive activism, and in many ways a companion piece to Guthrie�s folk anthem �This Land Is Your Land,� House of Earth is a searing portrait of hardship and hope set against a ravaged landscape. Combining the moral urgency and narrative drive of John Steinbeck with the erotic frankness of D. H. Lawrence, here is a powerful tale of America from one of our greatest artists.
An essay by bestselling historian Douglas Brinkley and Johnny Depp introduce House of Earth, the inaugural title in Depp�s imprint at HarperCollins, Infinitum Nihil.
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Chris Penn Age: 40character actor who played supporting—but memorable—roles in the films Reservoir Dogs, Starsky & Hutch, and Rush Hour. His brother is the actor Sean Penn. Died: Los Angeles, Jan. 24, 2006Information Please® Database, © 2007 Pearson Education, Inc. All rights reserved.
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Eastern Illinois University - University Professionals of Illinois (EIU-UPI) is the Eastern chapter of University Professionals of Illinois. EIU-UPI is the collective bargaining agent for the faculty and academic support professionals at Eastern Illinois University. We advocate for the interests of our members, to make them a priority for the University, state, and nation.
As professionals and intellectuals, our interests include a commitment to students and to academic quality. As part of the labor movement we support the interests of all working people in the spirit of solidarity.
Join in the CUPB discussion
We urge all members of the EIU community to participate in the discussions of budget cuts taking place CUPB. See this link for dates, times, locations and agendas of Friday afternoon meetings: http://castle.eiu.edu/~eiucupb/
EIU-UPI Organizes Response to Benefit-Cutting Pension "Reform"
EIU-UPI delivered hundreds of postcards to Senator Righter's office urging a "No" vote on the pension "reform" bill. The fight against unfair and unconstitutional cuts to pensions now moves to the courts, while the state of Illinois still faces shortfalls in revenue caused by an unfair tax system that burdens working and middle class Illinois citizens (click the link below to find out more).
Ralph Martire of the Center for Tax and Budget Accountability presented to EIU faculty on the fiscal crisis in Illinois. The video presents important information proving that the fiscal crisis in Illinois is driven by unfair taxation policies, not pensions.
More information can be found on the "CPAL" link in the menu bar.
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If you face discipline on the job, you have the right to union representation. These are your "Weingarten Rights," Protected by the Supreme Court.
Tell your Supervisor : "If this discussion could in any way lead to my being disciplined or terminated, or affect my personal working conditions, I request that my union representative, officer, or steward by present at the meeting. Without representation, I choose not to answer any questions."
EIU Chapter President Ann Fritz: 217-581-6907
EIU Grievance Officer Eric Bollinger: 217-581-6653


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Vertical Slat Back Oak Chairs provide years of reliable service. The newest Mission Style Look classroom chairs, are made of the same solid red oak construction and are ideal for any and all types of classroom chair applications. Wooden chairs have mortise and tenon construction on all joints, all edges are rounded and sanded and make a great child chair. The backs are steam bent and the seats are saddled for contour and comfort. Manufactured completely in the USA.
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I'm in New York and haven't seen any in our local stores yet either! Sure are good though!!
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Gov. Cuomo to Host 'Executive Mansion Open House'
New Yorkers will now have the opportunity to nurse their New Year's Day hangovers with the Governor.
Earlier this afternoon, Governor Andrew Cuomo announced he is flinging open the doors of the executive mansion on January 1st to members of the general public, offering the rare meet-and-greet opportunity. But for those wishing to attend, Mr. Cuomo's office suggests packing light.
"Guests are reminded that weapons and controlled substances as well as bags, backpacks, luggage, parcels, briefcases, and like articles will be strictly prohibited from the event," the press release warned. "Photography and videography devices will not be allowed on the premises. Guests with prohibited articles will be turned away and no storage or 'check-in area' will be provided for such items." Read More


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Used in this layout: Bert and Mary


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UTC Press, 1985-04-17, Special Election Edition |
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Special Elections Supplement TNI Y7 f 1 l I i 3 I ' ' I ? s J I 1 ) J i Special Election Edition Press Conference By Lynn Fausett Following the April 12th announcement of preliminary voting results, a press conference was held with the two remaining parties. Both parties answered questions from the PRESS staff and general studentbody expressing their respective . stands on different concerns. As the conference wound to a close, both prospective presidents made final statements. Jay Deuel summed up his Survivors II party as, "happy, excited, and committed." Bobby Thomas of the Odyssey party said he felt, "the single most important issue is our willingness to work together as a council. During the course of the conference these questions were asked. Q. How do you feel about increasing the student government council to nine members? Survivors II party-Tony Marren said, "I'd just be B.S. you if I told you anything. I'd like to study it more. I'm for whatever makes the council run more smoothly." Odyssey party-Brian Samuels said, "I'm in favor of it because the graphic artist position was made non-salary. So that money that is saved can finance more council members. More man power will help get things done. Q. Will you, if elected, make an accounting to the studentbody of student fees spent? THE OFFICIAL STUDENT NEWSPAPER OF UTAH TECHNICAL Kathleen Plothow, editor-in-chief A. Bobby Thomas of the Odyssey party, "Definitely we would publish that information and make a regular accounting. " Jay Deuel of the Survivors II party, "It says in the constitution that students are privileged to see that information if they want to. Yes, if people wanted to see it, we would publish it." Q. How do you propose to unite the students more? A. Brian Woolsey-Survivor II party-" We would like to get an intercom system that would connect both Reveals it X of the Press, hands microphones schools. That way we could have announcements during class breaks." Bobby Thomas-Odyssey party-"They are not aware, we would set up bulletin boards for one thing. We would make an effort to get in contact more." Q. How do you feel about the name change to Utah Valley College of Technical and General Studies? A. Odyssey party-Bobby Thomas--" Personally I'm in favor of the change, but I will follow the opinion of the COLLEGE AT PROVO-OREM Candidates Ideas t to members of the finalist parties students." Survivor II party-Jay Deuel-"Because of the poll I conducted, I think most students want 'Technical' retained in the name and this new proposal has the word 'Techinical ' in it, so I 'm for it. " Q. How cohesive is your party? A. Jay Deuel-Survivors II party-"I don't like cliches. We are more friends that we are a cliche." Brian Samuels-Odyssey party--"We started organizing in November and we are very compatible. I feel Wednesday, April 17, 1985 Pi 1 t . . 4m for student office. cohesiveness is our strongest point." Q. How do you propose to increase student activity and involve students more? A. Bobby Thomas of the Odyssey party, "I will allow all the council members to get involved. I will make the council members do their jobs." Jay Deuel of the Survivors II party, "Awareness is first. A dance is not suited for everyone, each different section has different needs and we want to find their needs before we spend more money on a dance.
Object Description
Rating | |
Title | UTC Press, 1985-04-17, Special Election Edition |
Description | UTC Press was the name of the student newspaper for Utah Technical College at Provo/Orem from February 07, 1985 to June 1, 1987. |
Date.Original | 1985-04-17 |
Publisher | Utah Valley University |
Subject headings | Utah Technical College at Provo/Orem--History; College student newspapers and periodicals; |
Type | Text |
Format | application/pdf |
Source | The Press Pulse, 1985-04-17, Special Election Edition |
Language | eng |
Rights | Copyright 2013 Utah Valley University |
Item.Year | 1985 |
Item.Month | 04 |
Item.Day | 17 |
Genre | newspaper |
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text: At the Multimaterial Joining Workshop (held in Chicago, IL, on July 23, 2012), subject matter experts from industry and academia described their views on state-of-the-art technologies, opportunities, and manufacturing challenges. Multimaterial joining techniques are important to diverse industries such as automotive, aerospace, rail, consumer goods, and medical devices. Further development of multimaterial joining technologies can lead to more reliable and low-cost multimaterial joints and welds for commercial and industrial applications.


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Dec 27, 2010, 10:20 #1
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postgresql: adding an array function to an existing query
I have have a query that returns rows from a blog database that can match a number of different supplied criteria. The query works as expected to return the data I require.
A typical example of the sql:
Code SQL:SELECT cr.category_id , c.name AS category_name , p.post_id , p.added , p.title , ts_headline(ts.title, ts.ts_query) AS headline , ts.ts_query , ts.rank FROM ( SELECT post_id , title , url , added , ts_query , ts_rank_cd(search_tsv, ts_query) AS rank FROM public.posts, plainto_tsquery('this is a test post') AS ts_query WHERE search_tsv @@ ts_query ORDER BY rank DESC ) AS ts INNER JOIN public.category_relationships AS cr ON cr.post_id = ts.post_id INNER JOIN public.categories AS c ON cr.category_id = c.category_id INNER JOIN public.posts AS p ON cr.post_id = p.post_id WHERE cr.category_id = 9 AND p.added BETWEEN '2010-12-01 00:00:00' AND '2010-12-31 23:59:59' AND p.post_id IN ( SELECT post_id FROM public.tags WHERE tag = 'tag1' OR tag = 'tag2' OR tag = 'tag3') ORDER BY ts.rank, p.added DESC
While I can search for a blog post using the above based on tags as part of the search criteria, I do not include the tags in the returned data since I will get a row of data for a certain post_id for each tag that exists for that post_id.
What I would like to do is include the following as a subquery for returning a formatted string containing the tags for the relevant post:
Code SQL:SELECT array_to_string( array( SELECT tag FROM public.tags WHERE post_id = p.post_id ), ', ') AS tags
Of course, the error is obvious: the alias 'p' does not exist in the subquery scope. My dilemma is how do I incorporate this subquery into the larger query above and pull the p.post_id into the subquery? I'm sure I'm just being obtuse here, but any thoughts would be appreciatedDucharme's Axiom: "If you view your problem closely
enough, you will recognize yourself as part of the problem."
Dec 27, 2010, 13:24 #2
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Actually, I discovered that I am using the wrong query with that particular subquery. Sorry to anyone who pondered this for me. While I would still like to be able to use that subquery with the original sql I posted (where the post_id may or may not be known), the query I needed to run on the database is below (the context of which the post_id is always known):
Code SQL:SELECT p.* , cr.category_id , array_to_string( array( SELECT tag FROM public.tags WHERE post_id = 8 ), ', ' ) AS tags FROM public.posts AS p INNER JOIN public.category_relationships AS cr ON cr.post_id = p.post_id WHERE p.enabled IS TRUE AND p.post_id = 8;
Ducharme's Axiom: "If you view your problem closely
enough, you will recognize yourself as part of the problem."


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New Orleans Jazz Fest: Piano Fest Highlights 2011
Airs Monday, April 30 at 9:00 p.m. Piano Night: We look back on a night that honored the 88's. Piano Night - every year on the Monday night between the two weekends of the New Orleans Jazz and Heritage and Festival. This hour-long program includes music from Piano Heaven, a smaller stage for more intimate performances (Jessie McBride, Marco Benevento, Cynthia Chen) as well as the main stage at the House of Blues (Dr. Lonnie Smith, Victor Atkins, Bob Andrews, Tom Worrell), featuring full ensembles that change throughout the show.


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Have you ever been referred to as having "East Coast style?" While I'm a California native, there is some New Jersey blood in there somewhere. I certainly appreciate direct communication, and will occasionally employ the use of profanity to show that I really care. So what does "East Coast style" actually sound like? The clip below is an alternate take of an AFSCME union public service announcement where the voice over talent gives us an excellent demonstration.
4 thoughts on "Humor Break: East Coast Style"
not much different than midwest "chicago style"
Hysterical. Thanks for posting this.
What does it mean about me that I watched this 3 times? I need to go get a cheese steak sandwich and then ask someone "what are you looking at" if they glance at me.


text:
Found December 08, 2012 on
Larry Brown Sports:
Jerry Brown died in a car crash early Saturday morning when the vehicle his friend and teammate Josh Brent was driving hit a curb and flipped. Brent was arrested and is being charged with intoxicated manslaughter. What makes the story even more sad is that Brown indicated in a recent Facebook post that was on his way to becoming a father. He also talked about no longer enjoying the fast life. "Sometimes when I actually get a chance to get away from football I think of how bad I want a family (wife, children) and how the fast life isn't as fun as it use to be after living it for so long," Brown wrote in a post dated Nov. 29. "I mean I do have my lil one coming soon and don't get me wrong I'm overly excited to c her and b everything for her. Its just the circumstances I bring her into aren't nearly as ideal as I'd like. I had what I wanted in a woman just without the seed. That relationship has been soiled due to the seed I planted. My only hopes are to become a better man and build a...
Original Story:
Cowboys Jerry Brown shares last regrets on Facebook
Dallas Cowboys' Josh Brent: "Jerry Brown was my very best friend."
Dallas Cowboys celebrate with fallen teammates jersey
Cowboy charged after player dies in auto accident
Cowboys reportedly were partying at club before fatal car crash
Crash Kills Jerry Brown, Cowboys Teammate Josh Brent Arrested
New tragedy rocks NFL's regularly scheduled world
Cowboys Josh Brent released from jail on $500,000 bond
DWI: Done With It
Cowboys' Brent attends memorial
Cowboys able to handle emotions in big win
Cowboys' Brent 'devastated, filled with grief' over crash that killed teammate
Cowboys' Brent attending memorial
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Welcome to the English Language Institute
The mission of the English Language Institute Instructional Division is to provide English language instruction to members of the University of Michigan community that promotes effective academic and intercultural communication within and across disciplines at the University of Michigan.
See a video highlighting what ELI has to offer to International Students while they are students at the University of Michigan:
ELI Graduate Writing Clinic Fall 2013
Monday - Friday
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- Visiting Scholar Course for the Winter - ELI 502 Academic Communications Seminar January 7, 2014
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text:
This is neat...thanks for sharing Franny!
EIN posted the link for this site...
http://www.elvicities.com/~presleyinthepres/While sites like EIN bring you a round up of each day's most important Elvis news there is one site which records virtually all media references to Elvis which can be found online. The site is Presleys in the Press. EIN recommends it to all Elvis scholars and those interested in Elvis' continuing cultural impact. (News, Source: EIN)
This is neat...thanks for sharing Franny!
"Its An Elvis Thing Man, You Just Wouldn't Understand"
Thanks for the post Franny...very insightful!
Daddy, I miss you more every day. You will always be my hero..
Thank you Franny
I dream a world where man no other man
will scorn. Where LOVE will bless the earth
and peace its paths adorn...
Great site, I've never seen this one, thanks Franny.


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For centuries we have marked our dead with lifeless granite – graves inscribed with names, dates, perhaps small inscriptions about the departed. But stones do not tell stories.
Now, a funeral home in Britain is attaching tiny digital codes to headstones, giving visitors the chance to see, hear, even experience the lives of the dead. No longer are graveyard visitors limited to letters and numbers etched into tombstone.
Read more http://feeds.abcnews.com/click.phdo?i=2b7db8f8f341b09880458c3ab568f3cc


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Youth control local lanesNovember 20, 2012Jake Gorton was on top of his game Saturday at Riverside Lanes, firing a 691 series in the Riverside Majors League. Gorton's series included games of 195, 259 and 237. Down Rt.... No comments posted for this article.
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I believe there is an automatic 18% gratuity for each TS meal. If that's the case i think you would be handed a bill for that.
I just called a CM to ask about Gratuity with the Dining Plan. Gratuity with the dining plan is at the DISCRETION OF THE CUSTOMER!!! That is from a CM and their supervisor!!!
Setting the record straightL Anyone who has the Dining Plan for 2008 can TIP AT THEIR LEISURE!!!!
Last edited by djmasterscooter; 12-06-2007 at 06:24 PM. Reason: Got Official Information
"Do Not Go To Disney World With A Bottle Of Pepsi. We will never see you again, and I'm pretty sure that's a bad thing"
I believe there is an automatic 18% gratuity for each TS meal. If that's the case i think you would be handed a bill for that.
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Rory Powe, who shut down his formerly high-flying Modulus Europe fund last year amid a period of poor performance and high redemption requests for many hedge fund managers, has launched a European long/short equity fund that he says aims to incorporate many of the lessons he and his team learned last year.
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The Tipping Point: Enough is Enough in Cameroon
The impact of civil society activities in Cameroon is getting to its 'tipping point' as CSOs' efforts to hold public officials to account can no longer be ignored.
The war against corruption in Cameroon has reached a point where emerging groups of civil society are beginning to add their voices in denunciations and actions that sends the 'enough is enough' message to corrupt government agents. Each year civil society accounts for more than 1000 corruption denunciations received by the independent, national anti-corruption commission, exposing corrupt practices and corrupt officials. Since the establishment of a national anti-corruption coalition of civil society organisations (CSOs) against corruption, more knowledge about -fraud, corruption and abuse of power is being exposed to the public. This enables communities and law enforcement agencies to step in and bring the corrupt to book. Although Cameroon's political commitment against corruption has been tainted with 'witch hunting' and not the rooting out of systemic corrupt practices that have taken the public service hostage, several champions are emerging from within the country. The Minister of Higher Education, together with the executive secretary for the National Anti-Corruption Strategy at the National Anti-Corruption Commission, Vice Chancellors and Regional Delegates have started working together with anti-corruption CSOs to see the momentum against corruption reach a tipping point.
At a time when hope and belief are severely stretched and many ordinary citizens are excluded from the institutions and practice of democracy, anti-corruption CSOs are becoming a beacon of hope to the many who find the system of democracy in Cameroon working for the benefit of the elite or political class. A sustained relationship between civil society organisations and the national anti-corruption agency has been deepened to mobilise and include voices that are being left out of the political process. They aim to contribute to an improvement of participatory governance and effective war against corruption. In analyzing the emerging anti-corruption movement in Cameroon and crafting a way forward, Frank Vogl's book "Waging War on Corruption" is a good read for the National Anti-Corruption Commission of Cameroon (CONAC) and CSOs.
The transition of the National Anti-Corruption Commission in Cameroon to work with civil society in combating corruption is gradually paying off with the dividends of corrupt officials and their practices being exposed and published for sanctions. The fear of being exposed or caught is shifting perception and forcing attitude adjustments in the public service and amongst greedy elites. The strength of this exploit lies in the heart of an effective partnership between anti-corruption civil society organisations and CONAC. Although this collective effort with civil society in combating corruption in Cameroon is still a learning curve, multiple lessons can be learned from such an exercise. First, there has been a constructive engagement between the various key champions against corruption in both public and private sector institutions, including the CONAC, in building and encouraging a "watch dog mentality" within civil society. Second, it has ensured an effective presence of the Commission in remote areas where most corrupt activities related to infrastructure are perpetrated by contractors and local government officials through the aid of its civil society partners.
As a follow up statement to the 15th International Anti-Corruption Conference Declaration 'Don't let them get away with it,' the Focal Integrity Team for Cameroon (FITCAM) is reiterating that the extent and impunity with which public officials continue to embezzle and misappropriate public funds and resources in Cameroon's struggling economy, demands a review of Cameroon's implementation of the UNCAC. CONAC has confirmed this impunity in its latest report in 2012. The agency's report reveals astonishing levels of corruption in macro projects that are still in their elementary stages, notably the Lom-Pangar Electrification and the Kribi Deepsea port projects.
According to the state of corruption report released by CONAC in November 2012, over 2 billion CFAF are said to have leaked out from Lom-Pangar (Electrification) Project in Cameroon as a result of multiple fraud and corrupt practices during the project's feasibility studies, tender, contract award and follow up processes of its first execution phase. Cameroon is a country which envisages to achieve an emerging country status by 2035 and to accomplish this, the government commits a considerable portion of its resources to infrastructure projects that cost it billions of CFAF. There is no denying that without a deliberate effort to root out the corrupt practices depleting the same resources earmarked for growth and development, Cameroon will hardly transition itself to emerging country status. A second huge project of significance to its emerging status is the Kribi Deep Seaport project. The Kribi deep seaport, like the Lom-Pangar, project is also riddled with corrupt practices concealed in fraudulent compensations and payments to individuals whose properties were never destroyed by the Kribi seaport project. An analysis of the claimants of land titles around the deep seaport project by CONAC reveal that 65 percent of those who have been paid compensations are hardened white collar criminals who do not have property within the deep seaport area. The report confirms that the government has paid out an estimated 500 million to 2 billion CFAF worth of compensation funds.
Corruption from last year alone puts the amount of money embezzled from projects in the billions of CFAF. In their book Why Nations Fail Daron Acemoglu and James A. Robinson explain that politics is the process by which a society chooses the rules that will govern it. If the choice of Cameroon's governing politics is that of corruption, as seen in TI's Corruption Perception Index, civil society must step in.
By sustaining and deepening the already existing relationships with anti-corruption reform champions in both the public and private sectors, civil society organisations will be able to triple the gains of collective action against corruption in Cameroon. The 2012 report of CONAC exposing and denouncing corrupt practices in Cameroon may have signalled that the country is at its tipping point in the fight against corruption, but also that it is necessary for the Cameroonian government to introduce an Anti-Corruption Law in conformity with the same international instruments which it has ratified, such as the UN Convention Against Corruption.
About Stephen Asek
Stephen Asek is the country director of the International Governance Institute's Focal Integrity Team for Cameroon (IGI-FITCAM) and member of the Coalition.
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After three years of operation, the UNCAC Implementation Review Mechanism is off to a promising but uneven start. Transparency International has prepared a progress report on the review process, and... Read more
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A reporting procedure would be a valuable addition to the Review Mechanism for the UN Convention against Corruption (UNCAC) because it would provide useful information for follow-up efforts on serious... Read more
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Access to information is a key component of the UN Convention against Corruption (UNCAC) and a logical precondition for ensuring accountability in decision-making. The right of access to information consists... Read more
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Of the hundreds of billions of dollars illicitly acquired by corrupt officials in the last 15 years, no more than US$5 billion has been recovered and even less returned to... Read more