Evaluation of pilot jobs for Apache Spark applications on HPC clusters
Autor: | Tristan Glatard, Valerie Hayot-Sasson |
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Rok vydání: | 2019 |
Předmět: |
Job scheduler
FOS: Computer and information sciences Queueing theory Computer Science - Performance business.industry Computer science media_common.quotation_subject Big data Supercomputer computer.software_genre Scheduling (computing) Performance (cs.PF) Debugging Computer Science - Distributed Parallel and Cluster Computing Software deployment Operating system Distributed Parallel and Cluster Computing (cs.DC) business computer media_common |
Zdroj: | eScience |
DOI: | 10.48550/arxiv.1905.12720 |
Popis: | Big Data has become prominent throughout many scientific fields, and as a result, scientific communities have sought out Big Data frameworks to accelerate the processing of their increasingly data-intensive pipelines. However, while scientific communities typically rely on High-Performance Computing (HPC) clusters for the parallelization of their pipelines, many popular Big Data frameworks such as Hadoop and Apache Spark were primarily designed to be executed on dedicated commodity infrastructures. This paper evaluates the benefits of pilot jobs over traditional batch submission for Apache Spark on HPC clusters. Surprisingly, our results show that the speed-up provided by pilot jobs over batch scheduling is moderate to non-existent (0.98 on average) despite the presence of long queuing times. In addition, pilot jobs provide an extra layer of scheduling that complicates debugging and deployment. We conclude that traditional batch scheduling should remain the default strategy to deploy Apache Spark applications on HPC clusters. |
Databáze: | OpenAIRE |
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