The New UPC++ DepSpawn High Performance Library for Data-Flow Computing with Hybrid Parallelism
Not available until 2023-06-15
Use this link to citehttp://hdl.handle.net/2183/31002
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TitleThe New UPC++ DepSpawn High Performance Library for Data-Flow Computing with Hybrid Parallelism
Fraguela, B.B., Andrade, D. (2022). The New UPC++ DepSpawn High Performance Library for Data-Flow Computing with Hybrid Parallelism. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13350. Springer, Cham. https://doi.org/10.1007/978-3-031-08751-6_55
[Abstract] Data-flow computing is a natural and convenient paradigm for expressing parallelism. This is particularly true for tools that automatically extract the data dependencies among the tasks while allowing to exploit both distributed and shared memory parallelism. This is the case of UPC++ DepSpawn, a new task-based library developed on UPC++ (Unified Parallel C++), a library for parallel computing on a Partitioned Global Address Space (PGAS) environment, and the well-known Intel TBB (Threading Building Blocks) library for multithreading. In this paper we present and evaluate the evolution of this library after changing its engine for shared memory parallelism and adapting it to the newest version of UPC++, which differs very strongly from the original version on which UPC++ DepSpawn was developed. As we will see, while keeping the same high level of programmability, the new version is on average 9.3% faster than the old one, the maximum speedup being 66.3%.