Lucas A. Wilson
John A. Lockman
ABSTRACT
The possibility of hardware failures occurring during the execution of application software continues to increase along with the scale of modern systems. Existing parallel development approaches cannot effectively recover from these errors except by means of expensive checkpoint/restart files. As a result, many CPU hours of scientific simulation are lost due to hardware failures.
Relentless Computing is a data-oriented approach to software development that allows for many classes of distributed and parallel algorithms, from no data-sharing to intense data-sharing, to be solved in both loosely- and tightly- coupled environments. Each process requires no knowledge of the current runtime status of the others to begin contributing, meaning that the execution pool can shrink and grow, as well as recover from hardware failure, automatically.
We present motivations for the development of Relentless Computing, how it works, examples of using Relentless Computing to solve several types of problems, and initial scaling results.
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Index Terms
- Poster: The relentless computing paradigm: a data-oriented programming model for distributed-memory computation
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