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Feedback Directed Implicit Parallelism
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BibTeX
@MISC{Harris_feedbackdirected,
author = {Tim Harris and Satnam Singh},
title = {Feedback Directed Implicit Parallelism},
year = {}
}
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Abstract
In this paper we present an automated way of using spare CPU resources within a shared memory multi-processor or multi-core machine. Our approach is (i) to profile the execution of a program, (ii) from this to identify pieces of work which are promising sources of parallelism, (iii) recompile the program with this work being performed speculatively via a work-stealing system and then (iv) to detect at run-time any attempt to perform operations that would reveal the presence of speculation. We assess the practicality of the approach through an implementation based on GHC 6.6 along with a limit study based on the execution profiles we gathered. We support the full Concurrent Haskell language compiled with traditional optimizations and including I/O operations and synchronization as well as pure computation. We use 20 of the larger programs from the ‘nofib ’ benchmark suite. The limit study shows that programs vary a lot in the parallelism we can identify: some have none, 16 have a potential 2x speed-up, 4 have 32x. In practice, on a 4-core processor, we get 10-80 % speed-ups on 7 programs. This is mainly achieved at the addition of a second core rather than beyond this. This approach is therefore not a replacement for manual parallelization, but rather a way of squeezing extra performance out of the threads of an already-parallel program or out of a program that has not yet been parallelized.
Keyphrases
feedback directed implicit parallelism limit study full concurrent haskell language shared memory multi-processor work-stealing system pure computation multi-core machine automated way second core 4-core processor 10-80 speed-ups spare cpu resource nofib benchmark suite already-parallel program extra performance traditional optimization manual parallelization
