ABSTRACT

Exploiting parallelism is essential to making full use of computer systems, from phones to supercomputers. It is thus intrinsic to most applications today, and is becoming increasingly so with time, especially as hardware becomes more heterogeneous. Programming effective and performant parallel applications remains a serious challenge, however. Achieving both high productivity and high performance currently requires multiple experts. The project seeks to reduce this to an ordinary programmer. This problem is often approached along only one of two lines, "theory down", focusing on high-level parallel languages and the theory and practice of parallel algorithms, or "architecture up", focusing on rethinking abstractions at multiple layers, starting with the hardware. The project?s core novelties are (1) to unify these two approaches, combining their strengths to reduce the expertise needed to write performant parallel programs, and (2) to develop integrated techniques that can enable taking advantage of heterogeneous hardware. Realizing these novelties will require designing a "full-stack" approach to parallelism and innovation across the hardware/software stack. The project's impacts are (1) the development of techniques that dramatically simplify parallel programming, including for heterogeneous machines, putting it into the purview of the ordinary programmer, and (2) the development of systems and educational materials to teach this skill to broader audiences including students at the researchers' institutions.

The technical strategy of the project is to bridge high-level parallel languages, which allow clean expression and analysis of program parallelism, to heterogeneous, extensible hardware (modeled using FPGAs) through an integrated series of intermediate representations (IRs) of a program and of the hardware/software capabilities of the target platform. The design of these representations will be geared to avoid the information loss (going both up and down the compiler/runtime/OS/hardware stack) that currently hampers optimization at all levels. A new compilation model for high-level parallel languages is being developed that extensively leverages modern compiler technology, but also avoids "premature lowering" of parallel constructs, and "premature abstraction" of hardware and low-level software features. Benchmarks are beinge developed to measure the effectiveness of the approach.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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