CSR: Medium: Collaborative Research: Scaling the Implicitly Parallel Programming Model with Lifelong Thread Extraction and Dynamic Adaptation

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The microprocessor industry has moved toward multicore designs to leverage increasing transistor counts in the face of physical and micro-architectural limitations. Unfortunately, providing multiple cores does not translate into performance for most applications. Rather than pushing all the burden onto programmers, this project advocates the use of the implicitly parallel programming model to eliminate the laborious and error-prone process of explicit parallel programming. Implicit parallel programming leverages sequential languages to facilitate shorter development and debug cycles, and relies on automatic tools, both static compilers and run-time systems, to identify parallelism and customize it to the target platform. Implicit parallelism can be systematically extracted using: (1) decoupled softwarepipelining, a technique to extract the pipeline parallelism found in many sequential applications; (2) low-frequency and high-confidence speculation to overcome limitations of memory dependence analysis; (3) whole-program scope for parallelization to eliminate analysis boundaries; (4) simple extensions to the sequential programming model that give the programmer the power to refine the meaning of a program; (5) dynamic adaptation to ensure efficiency is maintained across changing environments. This project is developing the set of technologies to realize an implicitly parallel programming system with scalable, lifelong thread extraction and dynamic adaptation. At the broader level, the implicitly parallel programming approach will free programmers to consider the problems they are trying to solve, rather than forcing them to overcome the processor industry's failure to continue to scale performance. This approach will keep computers accessible, helping computing to have the same increasingly positive impact on other fields.