2005Unpublished venueRequires access

Performance analysis tools for large-scale Linux clusters

Zarka Cvetanovic

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Abstract

As cluster computer environments increase in size and complexity, it is becoming more challenging to analyze and identify factors that limit performance and scalability. Easy-to-use tools that help identify such bottlenecks are crucial for tuning applications and configuring systems for best performance. We present a collection of visualization tools, which allow users to monitor load on all cluster components simultaneously, with negligible overhead, and no changes in the application. We include examples where the tools have been used to identify bottlenecks within a cluster and improve performance. We provide several examples of application profiles gathered using the tools and outline the methodology for projecting performance of future cluster platforms.

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What this paper is about

As cluster computer environments increase in size and complexity, it is becoming more challenging to analyze and identify factors that limit performance and scalability. Easy-to-use tools that help identify such bottlenecks are crucial for tuning applications and configuring systems for best performance. We present a collection of visualization tools, which allow users to monitor load on all cluster components simultaneously, with negligible overhead, and no changes in the application. We include examples where the tools have been used to identify bottlenecks within a cluster and improve performance. We provide several examples of application profiles gathered using the tools and outline the methodology for projecting performance of future cluster platforms.

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Available abstract

As cluster computer environments increase in size and complexity, it is becoming more challenging to analyze and identify factors that limit performance and scalability. Easy-to-use tools that help identify such bottlenecks are crucial for tuning applications and configuring systems for best performance. We present a collection of visualization tools, which allow users to monitor load on all cluster components simultaneously, with negligible overhead, and no changes in the application. We include examples where the tools have been used to identify bottlenecks within a cluster and improve performance. We provide several examples of application profiles gathered using the tools and outline the methodology for projecting performance of future cluster platforms.

Key concepts: Computer science, Scalability, Cluster (spacecraft), Overhead (engineering), Visualization, Computer cluster, Distributed computing, Limit (mathematics)

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