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We describe our approach in augmenting the BEAGLE library for high-performance statistical phylogenetic inference to support concurrent computation of independent partial likelihoods arrays. Our solution involves identifying independent likelihood estimates in analyses of partitioned datasets and in proposed tree topologies, and configuring concurrent computation of these likelihoods via CUDA and OpenCLl frameworks. We evaluate the effect of each increase in concurrency on throughput performance for our partial likelihoods kernel for a four-state nucleotide substitution model on a variety of parallel computing hardware, such as NVIDIA and AMD GPUs, and Intel multicore CPUs, observing up to 16-fold speedups over our previous implementation. Finally, we evaluate the effect of these gains on an domain application program, mrbayes. For a partitioned nucleotide-model analysis we observe an average speedup for the overall run time of 2.1-fold over our previous parallel implementation, and 10-fold over the native mrbayes with sse.
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Usability and Performance Improvements in Hatchet
Performance analysis is critical for pinpointing bottlenecks in parallel applications. Several profilers exist to instrument parallel programs on HPC systems and gather performance data. Hatchet is an open-source Python library that can read profiling output of several tools, and enables the user to perform a variety of programmatic analyses on hierarchical performance profiles. In this paper, we augment Hatchet to support new features: a query language for representing call path patterns that can be used to filter a calling context tree, visualization support for displaying and interacting with performance profiles, and new operations for performing analyses on multiple datasets. Additionally, we present performance optimizations in Hatchet’s HPCToolkit reader and the unify operation to enable scalable analysis of large datasets.
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- Award ID(s):
- 1656958
- PAR ID:
- 10285529
- Date Published:
- Journal Name:
- 2020 IEEE/ACM International Workshop on HPC User Support Tools (HUST) and Workshop on Programming and Performance Visualization Tools (ProTools)
- Page Range / eLocation ID:
- 49 to 58
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/HUSTProtools51951.2020.00013
