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Python's ease of use and rich collection of numeric libraries make it an excellent choice for rapidly developing scientific applications. However, composing these libraries to take advantage of complex heterogeneous nodes is still difficult. To simplify writing multi-device code, we created Parla, a heterogeneous task-based programming framework that fully supports Python's scientific programming stack. Parla's API is based on Python decorators and allows users to wrap code in Parla tasks for parallel execution. Parla arrays enable automatic movement of data between devices. The Parla runtime handles resource-aware mapping, scheduling, and execution of tasks. Compared to other Python tasking systems, Parla is unique in its parallelization of tasks within a single process, its GPU context and resource-aware runtime, and its design around gradual adoption to provide easy migration of and integration into existing Python applications. We show that Parla can achieve performance competitive with hand-optimized code while improving ease of development.
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Data Integration Tasks on Heterogeneous Systems Using OpenCL
In the era of big data, many new algorithms are developed to try and find the most efficient way to perform computations with massive amounts of data. However, what is often overlooked is the preprocessing step for many of these applications. The Data Integration Benchmark Suite (DIBS) was designed to understand the characteristics of dataset transformations in a hardware agnostic way. While on the surface these applications have a high amount of data parallelism, there are caveats in their specification that can potentially affect this characteristic. Even still, OpenCL can be an effective deployment environment for these applications. In this work we take a subset of the data transformations from each category presented in DIBS and implement them in OpenCL to evaluate their performance for heterogeneous systems. For targeting heterogeneous systems, we take a common application and attempt to deploy it to three platforms targetable by OpenCL (CPU, GPU, and FPGA). The applications are evaluated by their average transformation data rate. We illustrate the advantages of each compute device in the data integration space along with different communications schemes allowed for host/device communication in the OpenCL platform.
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- PAR ID:
- 10108237
- Date Published:
- Journal Name:
- Proc. of 7th International Workshop on OpenCL
- Page Range / eLocation ID:
- 1 to 1
- 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/10.1145/3318170.3318187
