Scalable performance analysis: the Pablo performance analysis environment

Reed, Daniel A.; Roth, Philip C.; Aydt, Ruth; Shields, K.A.; Tavera, L.F.; Noe, Roger J.; Schwartz, Baron

doi:10.1109/splc.1993.365577

Cited by 174 publications

(127 citation statements)

References 2 publications

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“…These logs tend to be extremely large and care has to be taken to control the generated data volume for effective postmortem analysis. Vampir [1], Jumpshot [2], Paradyn [3], KOJAK [4], Pablo [5] and Projections [6] are examples of tools and systems that can use various forms of detailed event logs.…”

Section: B Types Of Existing Performance Analysis Toolsmentioning

confidence: 99%

Continuous performance monitoring for large-scale parallel applications

Dooley

Lee

Kalé

2009

2009 International Conference on High Performance Computing (HiPC)

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Abstract-Traditional performance analysis techniques are performed after a parallel program has completed. In this paper, we describe an online method for continuously monitoring the performance of a parallel program, specifically the fraction of the time spent in various activities as the program executes. Our implementation of both a visualization client and the parallel performance framework that gathers utilization data are described. The data gathering uses a scalable and asynchronous reduction with an appropriate lossless compressed data format. The overheads in the initial system are low, even when run on thousands of processors. The data gathering occurs in an out-ofband communication mechanism, interleaving itself transparently with the execution of the parallel application by leveraging a message-driven runtime system.

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Section: B Types Of Existing Performance Analysis Toolsmentioning

confidence: 99%

Continuous performance monitoring for large-scale parallel applications

Dooley

Lee

Kalé

2009

2009 International Conference on High Performance Computing (HiPC)

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“…The Paradyn [18] and Pablo [23] tools provide a wealth of performance metrics. One similar metric to Critical Path profiling is Normalized Processor Time [1].…”

Section: Related Workmentioning

confidence: 99%

Critical path profiling of message passing and shared-memory programs

Hollingsworth

1998

IEEE Trans. Parallel Distrib. Syst.

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Abstract-In this paper, we introduce a runtime, nontrace-based algorithm to compute the critical path profile of the execution of message passing and shared-memory parallel programs. Our algorithm permits starting or stopping the critical path computation during program execution and reporting intermediate values. We also present an online algorithm to compute a variant of critical path, called critical path zeroing, that measures the reduction in application execution time that improving a selected procedure will have. Finally, we present a brief case study to quantify the runtime overhead of our algorithm and to show that online critical path profiling can be used to find program bottlenecks.

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“…Such post mortem analysis based on trace files has been an important tool [2][3][4][8][9][10][11][13][14][15][18][19][20]22] for performance analysis. Many of these tools display a trace file as a GANTT chart, with the x-axis representing time and the y-axis process or thread number.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Format for Nearly Constant-Time Access to Arbitrary Time Intervals in Large Trace Files

Chan

Gropp

Lusk

2008

Scientific Programming

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Abstract.A powerful method to aid in understanding the performance of parallel applications uses log or trace files containing time-stamped events and states (pairs of events). These trace files can be very large, often hundreds or even thousands of megabytes. Because of the cost of accessing and displaying such files, other methods are often used that reduce the size of the tracefiles at the cost of sacrificing detail or other information.This paper describes a hierarchical trace file format that provides for display of an arbitrary time window in a time independent of the total size of the file and roughly proportional to the number of events within the time window. This format eliminates the need to sacrifice data to achieve a smaller trace file size (since storage is inexpensive, it is necessary only to make efficient use of bandwidth to that storage). The format can be used to organize a trace file or to create a separate file of annotations that may be used with conventional trace files. We present an analysis of the time to access all of the events relevant to an interval of time and we describe experiments demonstrating the performance of this file format.

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Scalable performance analysis: the Pablo performance analysis environment

Cited by 174 publications

References 2 publications

Continuous performance monitoring for large-scale parallel applications

Continuous performance monitoring for large-scale parallel applications

Critical path profiling of message passing and shared-memory programs

An Efficient Format for Nearly Constant-Time Access to Arbitrary Time Intervals in Large Trace Files

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