Partial Evaluation for Scientific Computing: The Supercomputer Toolkit Experience

We describe the key role played by partial evaluation in the Supercomputer Toolkit, a parallel computing system for scientific applications that effectively exploits the vast amount of parallelism exposed by partial evaluation. The Supercomputer Toolkit parallel processor and its associated partial evaluation-based compiler have been used extensively by scientists at M.I.T., and have made possible recent results in astrophysics showing that the motion of the planets in our solar system is chaotically unstable.

Exploiting the Parallelism Exposed by Partial Evaluation

We describe an approach to parallel compilation that seeks to harness the vast amount of fine-grain parallelism that is exposed through partial evaluation of numerically-intensive scientific programs. We have constructed a compiler for the Supercomputer Toolkit parallel processor that uses partial evaluation to break down data abstractions and program structure, producing huge basic blocks that contain large amounts of fine-grain parallelism. We show that this fine-grain prarllelism can be effectively utilized even on coarse-grain parallel architectures by selectively grouping operations together so as to adjust the parallelism grain-size to match the inter-processor communication capabilities of the target architecture.

A Parallelizing Compiler Based on Partial Evaluation

We constructed a parallelizing compiler that utilizes partial evaluation to achieve efficient parallel object code from very high-level data independent source programs. On several important scientific applications, the compiler attains parallel performance equivalent to or better than the best observed results from the manual restructuring of code. This is the first attempt to capitalize on partial evaluation's ability to expose low-level parallelism. New static scheduling techniques are used to utilize the fine-grained parallelism of the computations. The compiler maps the computation graph resulting from partial evaluation onto the Supercomputer Toolkit, an eight VLIW processor parallel computer.

Learning-Based Approach to Estimation of Morphable Model Parameters

We describe the key role played by partial evaluation in the Supercomputing Toolkit, a parallel computing system for scientific applications that effectively exploits the vast amount of parallelism exposed by partial evaluation. The Supercomputing Toolkit parallel processor and its associated partial evaluation-based compiler have been used extensively by scientists at MIT, and have made possible recent results in astrophysics showing that the motion of the planets in our solar system is chaotically unstable.

Exploiting the Parallelism Exposed by Partial Evaluation

We describe the key role played by partial evaluation in the Supercomputing Toolkit, a parallel computing system for scientific applications that effectively exploits the vast amount of parallelism exposed by partial evaluation. The Supercomputing Toolkit parallel processor and its associated partial evaluation-based compiler have been used extensively by scientists at MIT, and have made possible recent results in astrophysics showing that the motion of the planets in our solar system is chaotically unstable.