Computer Science and Engineering

Load-Balanced Pipeline ParallelismMd Kamruzzaman Steven Swanson Dean M. TullsenComputer Science and Engineering University of California, San Diego{mkamruzz,swanson,tullsen}@cs.ucsd.eduABSTRACT Accelerating a single thread in current parallel systems remainsa challenging problem, because sequential threads do not natu- rally take advantage of the additional cores. Recent work shows that automatic extraction of pipeline parallelism is an effective way to speed up single thread execution. However, two problems re- main challenging – load balancing and inter-thread communica- tion. This work shows new mechanism to exploit pipeline paral- lelism that naturally solves the load balancing and communication problems. This compiler-based technique automatically extracts the pipeline stages and executes them in a data parallel fashion, using token-based chunked synchronization to handle sequential stages. This technique provides linear speedup for several appli- cations, and outperforms prior techniques to exploit pipeline par- allelism by as much as 50%.Categories and Subject Descriptors D.3.4 [Programming Languages]: Processors–OptimizationGeneral Terms Languages, PerformanceKeywords pipeline parallelism, load-balancing, chip multiprocessors, locality, compilers1. INTRODUCTION The number of cores per die on multicore processors increaseswith each processor generation. However, many applications fail to scale with the increased hardware parallelism. Several factors account for this, but this research is most concerned with key ap- plications that are difficult to parallelize due to data dependences in the key loops, making the code highly sequentialPrevious work on decoupled software pipelining [18, 19, 21] ad- dresses this problem and shows that fine-grained pipeline paral- Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. SC’13, November 17–21, 2013, Denver, Colorado, USA. Copyright 2013 ACM 978-1-4503-2378-9/13/11 …$15.00. http://dx.doi.org/10.1145/2503210.2503295.lelism applied at the loop level can be very effective in speeding up some serial codes, including irregular codes like pointer chas- ing. In this case, the compiler automatically divides the loop into a set of pipeline stages (each stage can be sequential or parallel) and maps them to different cores to achieve parallel execution while still maintaining all the dependencies. However, several issues make the technique still challenging in practice. First, the cores of- ten remain underutilized because of the imbalance in the pipeline, sacrificing performance and wasting energy. Second, the technique sacrifices the existing locality between stages, communicating data that was originally local across cores, again sacrificing both per- formance and power/energy. Finally, the technique typically works best with the number of threads at least equal to the number of stages, requiring the compiler to know a priori the number of cores available, and causing inefficient execution when the counts do not match.This paper describes load-balanced pipeline parallelism (LBPP) which exploits the same pipeline parallelism as prior work, but assigns work to threads in a completely different manner, main- taining locality and naturally creating load balance. While prior pipeline parallelism approaches executes a different stage on each core, LBPP executes all the stages of a loop iteration on the same core, but achieves pipeline parallelism by distributing different iter- ations to the available cores and using token based synchronization to handle sequential stages. It groups together several iterations of a single stage, though, before it moves to the next stage.LBPP is inherently load-balanced, because each thread does the same work (for different iterations). It maintains locality because same-iteration communication never crosses cores. The generated code is essentially the same no matter how many cores are targeted – thus the number of stages and the thread count are decoupled and the thread count can be determined at runtime and even change dur- ing runtime. Prior techniques must recompile to perform optimally with a different core count.For more information on Computer Science and Engineering check on:https://en.wikipedia.org/wiki/Computer_science_and_engineering

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