Towards High Productivity and Performance for Irregular Applications in Chapel

Rolinger, Thomas B.; Craft, Joe; Krieger, Christopher D.; Sussman, Alan

doi:10.1109/scws55283.2021.00012

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…In addition, each of these tasks is going to execute on a remote locale using the on clause. Then, locale-specific variables are created, such as termination detection flags and state vector (lines [10][11][12]. A second coforall loop-based tasking construct is then used to exploit the intra-node parallel level, creating as many tasks as threads per locale (line 13).…”

Section: Parallel Distributed Dfs In Chapelmentioning

confidence: 99%

“…There exist also other related works in which the inter‐node level is considered as well. For instance, in Reference 12, Chapel‐ and MPI+OpenMP‐based implementations are compared using Breadth‐First Search (BFS) and PageRank applications. Chapel is proven

3.7\times

faster on average for BFS, but

1.3\times

slower for PageRank.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Getting a feedback on the suitability and limits of conventional and new languages targeting exascale supercomputers becomes critical. In the same spirit as References 10‐15, this paper aims at providing a useful data point to help practitioners gauge the difficult question of whether or not to invest time and effort into learning and using the Chapel programming language.…”

Section: Background and Related Workmentioning

confidence: 99%

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Parallel distributed productivity‐aware tree‐search using Chapel

Helbecque

Gmys

Melab

et al. 2023

Concurrency and Computation

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With the recent arrival of the exascale era, modern supercomputers are increasingly big making their programming much more complex. In addition to performance, software productivity is a major concern to choose a programming language, such as Chapel, designed for exascale computing. In this paper, we investigate the design of a parallel distributed tree‐search algorithm, namely P3D‐DFS, and its implementation using Chapel. The design is based on the Chapel's DistBag data structure, revisited by: (1) redefining the data structure for Depth‐First tree‐Search (DFS), henceforth renamed DistBag‐DFS; (2) redesigning the underlying load balancing mechanism. In addition, we propose two instantiations of P3D‐DFS considering the Branch‐and‐Bound (B&B) and Unbalanced Tree Search (UTS) algorithms. In order to evaluate how much performance is traded for productivity, we compare the Chapel‐based implementations of B&B and UTS to their best‐known counterparts based on traditional OpenMP (intra‐node) and MPI+X (inter‐node). For experimental validation using 4096 processing cores, we consider the permutation flow‐shop scheduling problem for B&B and synthetic literature benchmarks for UTS. The reported results show that P3D‐DFS competes with its OpenMP baselines for coarser‐grained shared‐memory scenarios, and with its MPI+X counterparts for distributed‐memory settings, considering both performance and productivity‐awareness. In the context of this work, this makes Chapel an alternative to OpenMP/MPI+X for exascale programming.

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Section: Parallel Distributed Dfs In Chapelmentioning

confidence: 99%

3.7\times

faster on average for BFS, but

1.3\times

slower for PageRank.…”

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Parallel distributed productivity‐aware tree‐search using Chapel

Helbecque

Gmys

Melab

et al. 2023

Concurrency and Computation

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Adaptive Prefetching for Fine-grain Communication in PGAS Programs

Rolinger,

Sussman

2024

2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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Supercharging the APGAS Programming Model with Relocatable Distributed Collections

Finnerty

Kawanishi

Kamada

et al. 2022

Scientific Programming

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In this article, we present our relocatable distributed collection library. Building on top of the AGPAS for Java library, we provide a number of useful intranode parallel patterns as well as the features necessary to support the distributed nature of the computation through clearly identified methods. In particular, the transfer of distributed collections’ entries between processes is supported via an integrated relocation system. This enables dynamic load-balancing capabilities, making it possible for programs to adapt to uneven or evolving cluster performance. The system we developed makes it possible to dynamically control the distribution and the data flow of distributed programs through high-level abstractions. Programmers using our library can, therefore, write complex distributed programs combining computation and communication phases through a consistent API. We evaluate the performance of our library against two programs taken from well-known Java benchmark suites, demonstrating superior programmability and obtaining better performance on one benchmark and reasonable overhead on the second. Finally, we demonstrate the ease and benefits of load balancing and a more complex application, which uses the various features of our library extensively.

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Towards High Productivity and Performance for Irregular Applications in Chapel

Cited by 3 publications

References 12 publications

Parallel distributed productivity‐aware tree‐search using Chapel

Parallel distributed productivity‐aware tree‐search using Chapel

Adaptive Prefetching for Fine-grain Communication in PGAS Programs

Supercharging the APGAS Programming Model with Relocatable Distributed Collections

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