Parallel Programmability and the Chapel Language

Abstract

In this paper we consider productivity challenges for parallel programmers and explore ways that parallel language design might help improve end-user productivity. We offer a candidate list of desirable qualities for a parallel programming language, and describe how these qualities are addressed in the design of the Chapel language. In doing so, we provide an overview of Chapel's features and how they help address parallel productivity. We also survey current techniques for parallel programming and describe ways in which we consider them to fall short of our idealized productive programming model.

Exploring Traditional and Emerging Parallel Programming Models Using a Proxy Application

“…Chamberlain [17] and Saha [23] present multiple models as a theoretical foundation to point out the benefits of the authors' own approach. However, no study exists in which a single, realistic application is used to compare a wide range of traditional and emerging programming models or approaches in a systematic way, covering a wide range of aspects, including expressiveness, performance, productivity and manageability.…”

“…Chapel is an emerging parallel language initiated under the DARPA HPCS program with the goal of improving programmer productivity [17]. Chapel is designed using a blockimperative syntax with optional support for object-oriented programming, type inference, and other productivity-oriented features.…”

“…A version of LULESH exists for GPUs with an implementation in CUDA [16]. Additionally, we have ported LULESH to four new and emerging programming models: Chapel [17], CHARM++ [10], Liszt [12] and Loci [18]. The Liszt port can generate both a multiprocessor version and a GPU-enabled version.…”

Automatic Task-Based Code Generation for High Performance Domain Specific Embedded Language

“…• Chapel [10] is a full-fledged programming language developed by Cray to ease parallel programming. Like NT 2 , Chapel uses abstractions for dataparallelism with objects named Arrays (equivalent of Tables), and abstractions for task-parallelism with objects named Synchronization variables (equivalent of Futures).…”

Language virtualization for heterogeneous parallel computing

“…Recently, the high-performance programming language designs Chapel [10], X10 [40] and Fortress [27] have emerged from a DARPA initiative. These languages combine sophisticated type systems with explicit control of location and concurrency.…”