Graph for Science: From API based Programming to Graph Engine based Programming for HPC
arxiv(2023)
摘要
Modern scientific applications predominantly run on large-scale computing
platforms, necessitating collaboration between scientific domain experts and
high-performance computing (HPC) experts. While domain experts are often
skilled in customizing domain-specific scientific computing routines, which
often involves various matrix computations, HPC experts are essential for
achieving efficient execution of these computations on large-scale platforms.
This process often involves utilizing complex parallel computing libraries
tailored to specific matrix computation scenarios. However, the intricate
programming procedure and the need for deep understanding in both application
domains and HPC poses significant challenges to the widespread adoption of
scientific computing. In this research, we observe that matrix computations can
be transformed into equivalent graph representations, and that by utilizing
graph processing engines, HPC experts can be freed from the burden of
implementing efficient scientific computations. Based on this observation, we
introduce a graph engine-based scientific computing (Graph for Science)
paradigm, which provides a unified graph programming interface, enabling domain
experts to promptly implement various types of matrix computations. The
proposed paradigm leverages the underlying graph processing engine to achieve
efficient execution, eliminating the needs for HPC expertise in programming
large-scale scientific applications. Our results show that the graph
engine-based scientific computing paradigm achieves performance comparable to
the best-performing implementations based on existing parallel computing
libraries and bespoke implementations. Importantly, the paradigm greatly
simplifies the development of scientific computations on large-scale platforms,
reducing the programming difficulty for scientists and facilitating broader
adoption of scientific computing.
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