Executing Optimized Irregular Applications Using Task Graphs within Existing Parallel Models

Krieger, Christopher D.; Strout, Michelle Mills; Roelofs, Jonathan; Bajwa, Amanreet

doi:10.1109/sc.companion.2012.43

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…Based on these patterns, a new schedule for execution is created and passed to an executor that runs the code according to the schedule generated by the inspector. Full sparse tiling (FST) [23], [24], [25] is an I/E optimization that improves temporal and spatial locality by placing the execution of loop iterations that access the same data, even across different original loops, together into a scheduling entity called a sparse tile. The tiles together perform the same computation iterations as the original code, just in an optimized order.…”

Section: A Full Sparse Tiling Overviewmentioning

confidence: 99%

“…For unstructured codes, there has been various inspector/executor strategies [49] that reschedule across loops to improve data locality while still providing parallelism [50], [24], [51], [25]. The term communication avoidance was coined by Demmel et al [51] to refer to such schedules, some of which have processors perform some amount of overlapped computation to avoid communication.…”

Section: Communication Avoidancementioning

confidence: 99%

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Loop Chaining: A Programming Abstraction for Balancing Locality and Parallelism

Krieger

Strout

Olschanowsky

et al. 2013

2013 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum

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Abstract-There is a significant, established code base in the scientific computing community. Some of these codes have been parallelized already but are now encountering scalability issues due to poor data locality, inefficient data distributions, or load imbalance. In this work, we introduce a new abstraction called loop chaining in which a sequence of parallel and/or reduction loops that explicitly share data are grouped together into a chain. Once specified, a chain of loops can be viewed as a set of iterations under a partial ordering. This partial ordering is dictated by data dependencies that, as part of the abstraction, are exposed, thereby avoiding inter-procedural program analysis. Thus a loop chain is a partially ordered set of iterations that makes scheduling and determining data distributions across loops possible for a compiler and/or run-time system. The flexibility of being able to schedule across loops enables better management of the data locality and parallelism tradeoff. In this paper, we define the loop chaining concept and present three case studies using loop chains in scientific codes: the sparse matrix Jacobi benchmark, a domain-specific library, OP2, used in full applications with unstructured grids, and a domain-specific library, Chombo, used in full applications with structured grids. Preliminary results for the Jacobi benchmark show that a loop chain enabled optimization, full sparse tiling, results in a speedup of as much as 2.68x over a parallelized, blocked implementation on a multicore system with 40 cores.

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Section: A Full Sparse Tiling Overviewmentioning

confidence: 99%