Data Centric Performance Measurement Techniques for Chapel Programs

Zhang, Hui; Hollingsworth, Jeffrey K.

doi:10.1109/ipdps.2017.37

Cited by 3 publications

(1 citation statement)

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“…There has also been work on developing techniques to more effectively measure the performance of Chapel programs, where a data-centric view of performance data is studied as opposed to more traditional code-centric views [15]. In our work, we employed code-centric profiling to identify performance bottlenecks via gprof and source-code level timers.…”

Section: Related Workmentioning

confidence: 99%

Parallel Sparse Tensor Decomposition in Chapel

Rolinger

Simon

Krieger

2018

2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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In big-data analytics, using tensor decomposition to extract patterns from large, sparse multivariate data is a popular technique. Many challenges exist for designing parallel, high performance tensor decomposition algorithms due to irregular data accesses and the growing size of tensors that are processed. There have been many efforts at implementing shared-memory algorithms for tensor decomposition, most of which have focused on the traditional C/C++ with OpenMP framework. However, Chapel is becoming an increasingly popular programing language due to its expressiveness and simplicity for writing scalable parallel programs. In this work, we port a state of the art C/OpenMP parallel sparse tensor decomposition tool, SPLATT, to Chapel. We present a performance study that investigates bottlenecks in our Chapel code and discusses approaches for improving its performance. Also, we discuss features in Chapel that would have been beneficial to our porting effort. We demonstrate that our Chapel code is competitive with the C/OpenMP code for both runtime and scalability, achieving 83%-96% performance of the original code and near linear scalability up to 32 cores.

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Section: Related Workmentioning

confidence: 99%