Parallel algorithms on a fixed number of processors
There are several models, widely used in practice, that might be of interest to you.  Almost all of them could be viewed as generalizations of Amdahl's law.  They take two parameters, $P$ (number of processing elements) and $N$ size of the input.  Several conferences in which you are likely to find these types of analyses are the ACM Symposium on Parallel Algorithms and Architectures (SPAA), and the ACM SIGPLAN Symposium on the Principles and Practice of Paralllel Proogramming (PPoPP).

The goal with these models is generally to understand the tradeoff, for a specific algorithm, between the increased parallelism available from larger $P$ with the increased synchronization and communication costs that come along with larger $P$.

The first one that got major attention was


  Valiant, Leslie G: A bridging model for parallel computation, Communications of the ACM, 33(8):103-111, 1990.


Valiant assumes that your algorithm moves between phases of completely parallel work, a phase of barrier synchronization during which the processors communicate, and then the next phase of completely parallel work.  For each computation phase you assume it is going to take time inversely proportional to $P$.  For the synchronization phases you usually want to take into account load imbalance, and the latency of the communication phases is going to depend both on the structure of your communication network and how good a job you can do at minimizing the distance between communicating processors.

The paper


  Culler, D; Karp, R; Patterson, D; Sahay, A; Schauser, KE, Santos, E; Subramonian, R; von Eicken, T: $LogP$: Towards a Realistic Model of Parallel Computation, ACM Symp Principles and Practice of Parallel Programming, (PPOPP-4):1-12, 1993.


built on Valiant's model by carefully taking into account the $L$atency between nodes, the $o$verhead of sending a message, and the $g$ap required between send operations (a measure of bandwidth), in addition to $P$.  This is too detailed for some uses, but can be useful in cases where it is important for the algorithm designer to choose between many small messages and fewer long messages.

Finally I'll mention


  Blumofe, Robert D; Leiserson, Charles E: Scheduling Multithreaded Computations by Work Stealing, IEEE FOCS, 1994.


which gives a small generalization of Amdahl's law, pointing out that execution time is bounded (below) by $T_P = O(T_1/P + T_\infty)$, where $T_1$ is the minimum serial execution time and $T_\infty$ would be the minimum execution time with an infinite (i.e., arbitrarily large) number of processors and no communication delays.  (Another way to look at $T_\infty$ is that it is the critical path through the algorithm.)