I'm currently looking into parallel methods for ODE integration. There is a lot of new and old literature out there describing a wide range of approaches, but I haven't found any recent surveys or overview articles describing the topic in general.There's the book by Burrage [1], but it's almost 20 years old and hence does not cover many of the more modern ideas like the parareal algorithm.[1] K. Burrage, Parallel and Sequential Methods for Ordinary Differential Equations, Clarendon Press, Oxford, 1995
What's the state of the art in parallel ODE methods?
parallel computing;ode;numerical analysis
I'm not aware of any recent overview articles, but I am actively involved in the development of the PFASST algorithm so can share some thoughts.There are three broad classes of time-parallel techniques that I am aware of:across the method  independent stages of RK or extrapolation integrators can be evaluated in parallel; see also the RIDC (revisionist integral deferred correction algorithm)across the problem  waveform relaxationacross the time-domain  Parareal; PITA (parallel in time algorithm); and PFASST (parallel full approximation scheme in space and time).Methods that parallelize across the method usually perform very close to spec but don't scale beyond a handful of (time) processors.  Typically they are relatively easier to implement than other methods and are a good if you have a few extra cores lying around and are looking for predictable and modest speedups.Methods that parallelize across the time domain include Parareal, PITA, PFASST.  These methods are all iterative and are comprised of inexpensive (but inaccurate) coarse propagators and expensive (but accurate) fine propagators.  They achieve parallel efficiency by iteratively evaluating the fine propagator in parallel to improve a serial solution obtained using the coarse propagator. The Parareal and PITA algorithms suffer from a rather unfortunate upper bound on their parallel efficiency $E$: $E < 1/K$ where $K$ is the number of iterations required to obtain convergence throughout the domain.  For example, if your Parareal implementation required 10 iterations to converge and you are using 100 (time) processors, the largest speedup you could hope for would be 10x.  The PFASST algorithm relaxes this upper bound by hybridizing the time-parallel iterations with the iterations of the Spectral Deferred Correction time-stepping method and incorporating Full Approximation Scheme corrections to a hierarchy of space/time discretizations. Lots of games can be played with all of these methods to try and speed them up, and it seems as though the performance of these across-the-domain techniques depends on what problem you are solving and which techniques are available for speeding up the coarse propagator (coarsened grids, coarsened operators, coarsened physics etc.).Some references (see also references listed in the papers):This paper demonstrates how various methods can be parallelised across the method:  A theoretical comparison of high order explicit Runge-Kutta, extrapolation, and deferred correction methods; Ketcheson and Waheed.This paper also shows a nice way of parallelizing across the method, and introduces the RIDC algorithm: Parallel high-order integrators; Christlieb, MacDonald, Ong.This paper introduces the PITA algorithm: A Time-Parallel Implicit Method for Accelerating the Solution of Nonlinear Structural Dynamics Problems; Cortial and Farhat.There are lots of papers on Parareal (just Google it).Here is a paper on the Nievergelt method: A minimal communication approach to parallel time integration; Barker.This paper introduces PFASST: Toward an efficient parallel in time method for partial differential equations; Emmett and Minion; This papers describes a neat application of PFASST: A massively space-time parallel N-body solver; Speck, Ruprecht, Krause, Emmett, Minion, Windel, Gibbon.I have written two implementations of PFASST that are available on the 'net: PyPFASST and libpfasst.