The Chapel Parallel Programming Language

 

Publications and Papers

Featured Publications

Chapel HyperGraph Library (CHGL) [slides]. Louis Jenkins, Tanveer Bhuiyan, Sarah Harun, Christopher Lightsey, David Mentgen, Sinan Aksoy, Timothy Stavenger, Marcin Zalewski, Hugh Medal, and Cliff Joslyn. 2018 IEEE High Performance Extreme Computing Conference (HPEC '18). September 25–27, 2018.
This paper describes the design and implementation of a HyperGraph library provided as a scalable distributed data structure.
LAPPS: Locality-Aware Productive Prefetching Support for PGAS. Engin Kayraklioglu, Michael Ferguson, and Tarek El-Ghazawi. ACM Transactions on Architecture and Code Optimizations (ACM TACO). Volume 15, Issue 3. September 2018.
This paper describes a high-level, easy-to-use language feature to improve data locality efficiently.
Chapel Comes of Age: Productive Parallelism at Scale [slides (with outtakes)]. Brad Chamberlain, Elliot Ronaghan, Ben Albrecht, Lydia Duncan, Michael Ferguson, Ben Harshbarger, David Iten, David Keaton, Vassily Litvinov, Preston Sahabu, and Greg Titus. CUG 2018, Stockholm Sweden, May 22, 2018.
This paper describes the progress that has been made with Chapel since the HPCS program wrapped up.
(also see the publications at CHIUW, Chapel's annual workshop)

Other Recent Publications

Chapel Aggregation Library (CAL) [slides]. Louis Jenkins, Marcin Zalewski, and Michael Ferguson. Parallel Applications Workshop, Alternatives to MPI (PAW-ATM 2018), held at SC18. November 16, 2018.
This paper describes a minimal, generic, and easy-to-use aggregation library written entirely in Chapel, for Chapel.
ChplBlamer: A Data-centric and Code-centric Combined Profiler for Multi-locale Chapel Programs [slides]. Hui Zhang and Jeffrey K. Hollingsworth. In Proceedings of the 32nd ACM International Conference on Supercomputing (ICS'18), pages 252–262. June 2018.
This paper describes a tool that uses a combination of data-centric and code-centric information to relate performance profiling information back to user-level data structures and source code in Chapel programs.
APAT: an access pattern analysis tool for distributed arrays. Engin Kayraklioglu and Tarek El-Ghazawi. In Proceedings of the 15th ACM International Conference on Computing Frontiers (CF'18), pages 248–251. May 2018.
This paper proposes a high-level, data-centric profiler to analyze how distributed arrays are used by each locale.

Chapel Overviews

Chapel chapter, Bradford L. Chamberlain, Programming Models for Parallel Computing, edited by Pavan Balaji, published by MIT Press, November 2015.
This is currently the best introduction to Chapel's history, motivating themes, and features. It also provides a brief summary of current and future activities at the time of writing. An early pre-print of this chapter was made available under the name A Brief Overview of Chapel.
Parallel Programmability and the Chapel Language Bradford L. Chamberlain, David Callahan, Hans P. Zima. International Journal of High Performance Computing Applications, August 2007, 21(3): 291-312.
This is an early overview of Chapel's themes and main language concepts.

Multiresolution Chapel Features

User-Defined Parallel Zippered iterators in Chapel [slides]. Bradford L. Chamberlain, Sung-Eun Choi, Steven J. Deitz, Angeles Navarro. PGAS 2011: Fifth Conference on Partitioned Global Address Space Programming Models, October 2011.
This paper describes how users can create parallel iterators that support zippered iteration in Chapel, demonstrating them via several examples that partition iteration spaces statically and dynamically.
Authoring User-Defined Domain Maps in Chapel [slides]. Bradford L. Chamberlain, Sung-Eun Choi, Steven J. Deitz, David Iten, Vassily Litvinov. CUG 2011, May 2011.
This paper builds on our HotPAR 2010 paper by describing the programmer's role in implementing user-defined distributions and layouts in Chapel.
User-Defined Distributions and Layouts in Chapel: Philosophy and Framework [slides]. Bradford L. Chamberlain, Steven J. Deitz, David Iten, Sung-Eun Choi. 2nd USENIX Workshop on Hot Topics in Parallelism (HotPar'10), June 2010.
This paper describes our approach and software framework for implementing user-defined distributions and memory layouts using Chapel's domain map concept.

Chapel Tools

Data-Centric Performance Measurement Techniques for Chapel Programs [slides]. Hui Zhang and Jeffrey K. Hollingsworth. the 31st IEEE International Parallel and Distributed Processing Symposium, Orlando FL, May 30, 2017.
This paper describes a profiling tool that associates performance with data structures (e.g., arrays) rather than code locations and its use in optimizing Chapel code.

Chapel Explorations

Parameterized Diamond Tiling for Stencil Computations with Chapel Parallel Iterators [slides]. Ian J. Bertolacci, Catherine Olschanowsky, Ben Harshbarger, Bradford L. Chamberlain, David G. Wonnacott, Michelle Mills Strout. ICS 2015, June 2015.
This paper explores the expression of parameterized diamond-shaped time-space tilings in Chapel, demonstrating competitive performance with C+OpenMP along with significant software engineering benefits due to Chapel's support for parallel iterators.
Performance Portability with the Chapel Language. Albert Sidelnik, Saeed Maleki, Bradford L. Chamberlain, María J. Garzarán, David Padua. IPDPS 2012, May 2012.
This paper describes the use of Chapel to target GPUs and multicore processors using a unified set of language concepts.

Chapel Optimizations

LLVM-based Communication Optimizations for PGAS Programs. Akihiro Hayashi, Jisheng Zhao, Michael Ferguson, Vivek Sarkar. 2nd Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC2), November 2015.
This paper describes how LLVM passes can optimize communication in PGAS languages like Chapel. In particular, by representing potentially remote addresses using a distinct address space, existing LLVM optimization passes can be used to reduce communication.
Caching Puts and Gets in a PGAS Language Runtime [slides]. Michael P. Ferguson, Daniel Buettner. 9th International Conference on Partitioned Global Address Space Programming Models (PGAS 2015), Sept 2015.
This paper describes an optimization implemented for Chapel in which the runtime library aggregates puts and gets in accordance with Chapel's memory consistency model in order to reduce the potential overhead of doing fine-grained communications.

Chapel Philosophy

Multiresolution Languages for Portable yet Efficient Parallel Programming, Bradford L. Chamberlain, whitepaper, October 2007.
This is a position paper written in Q&A format that serves as the first written description of Chapel's multiresolution language design philosophy.

Archived Publications and Papers