Compiler Support for Exploiting Coarse-Grained Pipelined Parallelism

Du, Wei; Ferreira, Renato; Agrawal, Gagan

doi:10.1145/1048935.1050159

Cited by 24 publications

(21 citation statements)

References 35 publications

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“…However, the idea of coarse-grained software pipelineing has been explored before. [28] has proposed an algorithm that automatically breaks up nested loops, function calls, and control code into sets of coarse-grain filters based on a cost model. And, these sets of filters are then generated for parallel execution.…”

Section: Related Workmentioning

confidence: 99%

Stream Compilation for Real-Time Embedded Multicore Systems

Choi

Lin

Chong

et al. 2009

2009 International Symposium on Code Generation and Optimization

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Abstract-Multicore systems have not only become ubiquitous in the desktop and server worlds, but are also becoming the standard in the embedded space. Multicore offers programability and flexibility over traditional ASIC solutions. However, many of the advantages of switching to multicore hinge on the assumption that software development is simpler and less costly than hardware development. However, the design and development of correct, high-performance, multi-threaded programs is a difficult challenge for most programmers. Stream programming is one model that has wide applicability in the multimedia, signal processing, and networking domains. Streaming is convenient for developers because it separates the creation of actors, or functions that operate on packets of data, from the flow of data through the system. However, stream compilers are generally ineffective for embedded systems because they do not handle strict resource or timing constraints. Specifically, real-time deadlines and memory size limitations are not handled by conventional stream partitioning and scheduling techniques. This paper introduces the SPIR compiler that orchestrates the execution of streaming applications with strict memory and timing constraints. Software defined radio or SDR is chosen as the application space to illustrate the effectiveness of the compiler for mapping applications onto the IBM Cell platform.

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Section: Related Workmentioning

confidence: 99%

Stream Compilation for Real-Time Embedded Multicore Systems

Choi

Lin

Chong

et al. 2009

2009 International Symposium on Code Generation and Optimization

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“…Typically, the number of such atomic filters is much larger than the number of stages in the execution environment, or the number of filters we need to generate. In our earlier work, we had described how our compiler could obtain the sequence of atomic filters from a data parallel program [9]. Alternatively, an application developer may also specify such atomic filters.…”

Section: Filter Decomposition Problemmentioning

confidence: 99%

“…There are two datasets we used for this set of experiments, which were generated by an environmental simulator ParSSim [2] and were previously used in earlier studies also [4,9,8]. These datasets comprised grid data for 10 time-steps, and were 1.5 GB and 6 GB, respectively.…”

Section: Experiments With Iso-surfacementioning

confidence: 99%

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Filter decomposition for supporting coarse-grained pipelined parallelism

Agrawal

2005

2005 International Conference on Parallel Processing (ICPP'05)

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We consider the filter decomposition problem in supporting coarse-grained pipelined parallelism. This form of parallelism is suitable for data-driven applications in scenarios where the data is available on a repository or a data collection site on the internet, and the final results are required on a user's desktop. A filter decomposition algorithm takes an application divided into a sequence of atomic filters, and maps them into a given number of filters.We propose three polynomial time algorithms for this problem. Dynamic programming algorithm MIN ONETRIP optimizes the one trip cost for a packet passing through the pipeline. MIN BOTTLENECK is also a dynamic programming algorithm, which minimizes the time spent on the bottleneck stage. Finally, MIN TOTAL is an approximate greedy algorithm which tries to minimize the total execution time.The results show that our heuristic algorithms work quite well in practice, with the possible exception of MIN ONETRIP when the number of packets is large. However, the relative performance of the algorithms is not always what we would expect, because of the certain limitations in how we model the problem.

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“…The constraints that need to be observed are also specified. In our current implementation, a data parallel dialect of Java is taken as the basis [20]. A special class is defined and it is required that all the adaptive parts of the application must extend this class.…”

Section: Introductionmentioning

confidence: 99%

Language and Compiler Support for Adaptive Applications

Agrawal

Proceedings of the ACM/IEEE SC2004 Conference

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There exist many application classes for which the users have significant flexibility in the quality of output they desire. At the same time, there are other constraints, such as the need for real-time response or limit on the consumption of certain resources, which are more crucial. This paper provides a combined language/compiler and runtime solution for supporting adaptive execution of these applications, i.e., to allow them to achieve the best precision while still meeting the specified constraint at runtime.The key idea in our language extensions is to have the programmers specify adaptation parameters, i.e, the parameters whose values can be varied within a certain range. A program analysis algorithm states the execution time of an application component as a function of the values of the adaptation parameters and other runtime constants. These constants are determined by initial runs of the application in the target environment. We integrate this work with our previous work on supporting coarse-grained pipelined parallelism, and thus support adaptive execution for data-intensive applications in a distributed environment.Our experimental results on three applications have shown that our combined compile-time/runtime model can predict the execution times quite well, and therefore, support adaptation to meet a variety of constraints.

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Compiler Support for Exploiting Coarse-Grained Pipelined Parallelism

Cited by 24 publications

References 35 publications

Stream Compilation for Real-Time Embedded Multicore Systems

Stream Compilation for Real-Time Embedded Multicore Systems

Filter decomposition for supporting coarse-grained pipelined parallelism

Language and Compiler Support for Adaptive Applications

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