6 Concluding Remarks Processors Granularity Generator Utilization Executor Communication Eduction Local Function Utilization 1 84 1.2 0.0

Messages: a mechanism for intergrated communication and computation. 25 cost of implementing demand-driven eduction in the generator is negligible. Clearly, it is important to not only exercise the GLU programming model in expressing diverse applications, but also to develop abstract architectures that are inherently more scalable, thus eeectively exploiting less coarse-grain parallelism. References 1] E.A. Ashcroft. Dataaow and Eduction: Data-driven and demand-driven distributed computation. A descriptive and prescriptive model for dataaow semantics. 24 cessors with relatively medium-grain parallelism, sustaining this eeciency on a large workstation cluster is only possible if remote computing time is at least two orders of magnitude larger than the associated communication time. This study also connrms the fairly obvious intuition that the generator is the bottleneck of the generator/executor abstract architecture. There are two aspects to the generator bottleneck: one is the cost of communicating with all the executors and the other is the time spent in local function execution (i.e., the serial part of the application). And importantly, the generator spends only nominal amount of its time on eductive evaluation of the application kernels that were studied. The main contribution of this chapter is the validation of a dataaow-based approach to programming conventional parallel computers by showing that standard parallel application kernels can be succinctly expressed while reusing existing sequential code and that these kernels exhibit parallel performance which are comparable to those of equivalent kernels developed in lower-level explicit parallel programming systems. In particular, we have described a coarse-grain dataaow system for programming conventional parallel computers. The system is based on a hybrid model of programming (GLU) that consists of a multidimensional dataaow language Lucid for composing implicitly parallel programs using imperatively-speciied functions that specify computations. We have shown how GLU can be used to succinctly compose applications with substantial inherent parallelism from existing sequential code with only nominal modiications. We have shown how GLU programs can be mapped to abstract archi-tectures which then can be compiled to speciic target computers. Using the generator/executor abstract architecture, we have considered the ee-ciency of GLU programs executing in parallel on workstation networks. The speedup eeciency observed (speedup being relative to sequential execution on one processor) suggests that the generator eventually becomes the bottleneck mainly because communication cost and local function execution cost begin to dominate. We also observe that coarser granularity of parallelism means that better scaling of performance. Importantly, the performance study shows that GLU …