A Learning-based Selection for Portfolio Scheduling of Scientific Applications on Heterogeneous Computing Systems

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Provided by: American V-King Scientific Publishing
Topic: Networking
Format: PDF
The execution of computationally intensive parallel applications in heterogeneous environments, where the quality and quantity of computing resources available to a single user continuously change, often leads to irregular behavior, in general due to variations of algorithmic and systemic nature. To improve the performance of scientific applications, loop scheduling algorithms are often employed for load balancing of their parallel loops. However, it is challenging to select the most robust scheduling algorithms that guarantee an optimized execution of scientific applications on large-scale computing systems.
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