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Intermediate Language Extensions for Parallelism

Jisheng Zhao, Vivek Sarkar
Dept. of Computer Science, Rice University
The 5th workshop on Virtual Machines and Intermediate Languages, A workshop at SPLASH 2011, 2011

@article{zhao2011intermediate,

   title={Intermediate Language Extensions for Parallelism},

   author={Zhao, J. and Sarkar, V.},

   year={2011}

}

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An Intermediate Language (IL) specifies a program at a level of abstraction that includes precise semantics for state updates and control flow, but leaves unspecified the low-level software and hardware mechanisms that will be used to implement the semantics. Past ILs have followed the von Neumann execution model by making sequential execution the default, and by supporting parallelism with runtime calls for lower-level mechanisms such as threads and locks. Now that the multicore trend is making parallelism the default execution model for all software, it behooves us as a community to study the fundamental requirements in parallel execution models and explore how they can be supported by first-class abstractions at the IL level. In this paper, we introduce five key requirements for Parallel Intermediate Representations (PIRs): 1) Lightweight asynchronous tasks and communications, 2) Explicit locality, 3) Directed Synchronization with Dynamic Parallelism:, 4) Mutual Exclusion and Isolation with Dynamic Parallelism, and 5) Relaxed Exception semantics for Parallelism. We summarize the approach being taken in the Habanero Multicore Software Research project at Rice University to define a Parallel Intermediate Representation (PIR) to address these requirements. We discuss the basic issues of designing and implementing PIRs within the Habanero-Java (HJ) compilation framework that spans multiple levels of PIRs. By demonstrating several program optimizations developed in the HJ compilation framework, we show that this new PIR-based approach to compiler development brings robustness to the process of analyzing and optimizing parallel programs and is applicable to a wide range of task-parallelism programming models available today.
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