On using SCALEA for performance analysis of distributed and parallel programs

Truong, Hong-Linh; Fahringer, Thomas; Malony, Allen D.; Moritsch, H; Shende, Sameer

doi:10.1145/582034.582068

Cited by 16 publications

(9 citation statements)

References 17 publications

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“…In previous work [8], we presented a preliminary and very coarse grain classification of performance overheads which has been stimulated by [9]. Figure 2 shows our novel and substantially refined overhead classification which includes the following.…”

Section: Classification Of Temporal Overheadsmentioning

confidence: 99%

SCALEA: a performance analysis tool for parallel programs

Truong

Fahringer

2003

Concurrency and Computation

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SUMMARYMany existing performance analysis tools lack the flexibility to control instrumentation and performance measurement for code regions and performance metrics of interest. Performance analysis is commonly restricted to single experiments.In this paper we present SCALEA, which is a performance instrumentation, measurement, analysis, and visualization tool for parallel programs that supports post-mortem performance analysis. SCALEA currently focuses on performance analysis for OpenMP, MPI, HPF, and mixed parallel programs. It computes a variety of performance metrics based on a novel classification of overhead. SCALEA also supports multi-experiment performance analysis that allows one to compare and to evaluate the performance outcome of several experiments. A highly flexible instrumentation and measurement system is provided which can be controlled by command-line options and program directives. SCALEA can be interfaced by external tools through the provision of a full Fortran90 OpenMP/MPI/HPF frontend that allows one to instrument an abstract syntax tree at a very high-level with C-function calls and to generate source code. A graphical user interface is provided to view a large variety of performance metrics at the level of arbitrary code regions, threads, processes, and computational nodes for single-and multi-experiment performance analysis.

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Section: Classification Of Temporal Overheadsmentioning

confidence: 99%

SCALEA: a performance analysis tool for parallel programs

Truong

Fahringer

2003

Concurrency and Computation

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“…Some works discuss the performance analysis of OpenMP parallel application in the High Performance Computing field. SCALEA [5] allows the evaluation of the overheads introduced by parallel programming paradigms. It deals not only with OpenMP but also with Message Passing Interface (MPI) and High Performance Fortan (HPF).…”

Section: Introductionmentioning

confidence: 99%

Performance modeling of parallel applications on MPSoCs

Lattuada

Pilato

Tumeo

et al. 2009

2009 International Symposium on System-on-Chip

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Abstract-In this paper we present a new technique for automatically measuring the performance of tasks, functions or arbitrary parts of a program on a multiprocessor embedded system. The technique instruments the tasks described by OpenMP, used to represent the task parallelism, while ad hoc pragmas in the source indicate other pieces of code to profile. The annotations and the instrumentation are completely target-independent, so the same code can be measured on different target architectures, on simulators or on prototypes.We validate the approach on a single and on a dual LEON 3 platform synthesized on FPGA, demonstrating a low instrumentation overhead. We show how the information obtained with this technique can be easily exploited in a Hardware/Software design space exploration tool, by estimating, with good accuracy, the speed-up of a parallel applications given the profiling on the single processor prototype.

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“…A catalog of performance properties of hybrid programs was specified in [15]. Similar performance properties can be found in [4,5] and [6,7]. These typical properties form the basis for the ATS framework, which allows one to construct synthetic positive and negative test programs.…”

Section: Introductionmentioning

confidence: 99%

A test suite for parallel performance analysis tools

Gerndt¹,

Mohr²,

Träff³

2006

Concurrency and Computation

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SUMMARYParallel performance analysis tools must be tested as to whether they perform their task correctly, which comprises at least three aspects. First, it must be ensured that the tools neither alter the semantics nor distort the run-time behavior of the application under investigation. Next, it must be verified that the tools collect the correct performance data as required by their specification. Finally, it must be checked that the tools perform their intended tasks and detect relevant performance problems. Focusing on the latter (correctness) aspect, testing can be done using synthetic test functions with controllable performance properties, possibly complemented by real-world applications with known performance behavior. A systematic test suite can be built from synthetic test functions and other components, possibly with the help of tools to assist the user in putting the pieces together into executable test programs. Clearly, such a test suite can be highly useful to builders of performance analysis tools. It is surprising that, up until now, no systematic effort has been undertaken to provide such a suite. In this paper we describe the APART Test Suite (ATS) for checking the correctness (in the above sense) of parallel performance analysis tools. In particular, we describe a collection of synthetic test functions which allows one to easily construct both simple and more complex test programs with desired performance properties. We briefly report on experience with MPI and OpenMP performance tools when applied to the test cases generated by ATS.

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On using SCALEA for performance analysis of distributed and parallel programs

Cited by 16 publications

References 17 publications

SCALEA: a performance analysis tool for parallel programs

SCALEA: a performance analysis tool for parallel programs

Performance modeling of parallel applications on MPSoCs

A test suite for parallel performance analysis tools

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