A general predictive performance model for wavefront algorithms on clusters of SMPs

Hoisie, Adolfy; Lubeck, O.; Wasserman, Harvey J.; Petrini, Fabrizio; Alme, H. J.

doi:10.1109/icpp.2000.876127

Cited by 41 publications

(42 citation statements)

References 4 publications

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“…OpenMP provides threadlevel parallelism within MPI tasks. Sweep3D [8,13] is a solver for the 3-D, time-independent, particle transport equation on an orthogonal mesh and it uses a multidimensional wavefront algorithm for "discrete ordinates" deterministic particle transport simulation. Sweep3D benefits from multiple wavefronts in multiple dimensions, which are partitioned and pipelined on a distributed memory system.…”

Section: Applicationsmentioning

confidence: 99%

Scalable Analysis Techniques for Microprocessor Performance Counter Metrics

Ahn¹,

Vetter²

2002

ACM/IEEE SC 2002 Conference (SC'02)

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Contemporary microprocessors provide a rich set of integrated performance counters that allow application developers and system architects alike the opportunity to gather important information about workload behaviors. Current techniques for analyzing data produced from these counters use raw counts, ratios, and visualization techniques help users make decisions about their application performance. While these techniques are appropriate for analyzing data from one process, they do not scale easily to new levels demanded by contemporary computing systems. Very simply, this paper addresses these concerns by evaluating several multivariate statistical techniques on these datasets. We find that several techniques, such as statistical clustering, can automatically extract important features from the data. These derived results can, in turn, be fed directly back to an application developer, or used as input to a more comprehensive performance analysis environment, such as a visualization or an expert system.

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Section: Applicationsmentioning

confidence: 99%

Scalable Analysis Techniques for Microprocessor Performance Counter Metrics

Ahn¹,

Vetter²

2002

ACM/IEEE SC 2002 Conference (SC'02)

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show abstract

“…First it considers a specific class of parallel algorithms that use macro-pipelining techniques to exhibit parallelism in matrix computations. Models and implementations of such algorithms have been proposed both for distributed memory [1,7,9,11,12,16] and shared memory machines [2]. But these works focus on data that fit into memory.…”

Section: Related Workmentioning

confidence: 99%

“…Thereby, they allow for an overlap of computation and communication by reordering loops [8] and adding pipeline loops. These techniques can be used for several applications with wavefront computations like the ADI [9,12], Gauss-Seidel [1], SOR [11], or Sweep3D [7,16] algorithms.…”

Section: Introductionmentioning

confidence: 99%

Out-of-Core Wavefront Computations with Reduced Synchronization

Clauss

Gustedt

Suter

2008

16th Euromicro Conference on Parallel, Distributed and Network-Based Processing (PDP 2008)

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Matrix computation algorithms often exhibit dependencies between neighboring elements inside loop nests such that the frontier between computed elements and those to be computed wanders in form of a 'wave' through the matrix. Macro-pipelining techniques can achieve an efficient parallelization of such algorithms by overlapping communication and computation. Usually these techniques are limited to situations where all the data to be processed fits into main memory, whereas for larger data the I/O usage pattern for external storage requires special attention. The work [5] presented a first extension of the wavefront framework to these so-called out-of-core problems. The present paper proposes a redesign of their algorithm that minimizes both overhead and perturbations coming from communications. To tackle the issue of non-contiguous I/O, we also propose an optimized data layout. These two major modifications of the original algorithm eventually allow us to present a third improvement as our implementation shortens the transition phase between two consecutive iterations of the wavefront algorithm. Experiments performed with the PARXXL library show that we can significantly reduce the time lost during inefficient I/O operations and thus obtain faster computations.

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“…Sweep3D [8] is a 3D Cartesian geometry neutron transport code benchmark from the DOE's Accelerated Strategic Computing Initiative. As a procurement benchmark, this code has been extensively studied and tuned already [7], [10], [18], [20], [22]. The Gyrokinetic Toroidal Code (GTC) [11] is a particle-in-cell code that simulates turbulent transport of particles and energy.…”

Section: Case Studiesmentioning

confidence: 99%

Pinpointing and Exploiting Opportunities for Enhancing Data Reuse

Marin

Mellor-Crummey

2008

ISPASS 2008 - IEEE International Symposium on Performance Analysis of Systems and Software

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Abstract-The potential for improving the performance of data-intensive scientific programs by enhancing data reuse in cache is substantial because CPUs are significantly faster than memory. Traditional performance tools typically collect or simulate cache miss counts or rates and attribute them at the function level. While such information identifies program scopes that exhibit a large cache miss rate, it is often insufficient to diagnose the causes for poor data locality and to identify what program transformations would improve memory hierarchy utilization. This paper describes an approach that uses memory reuse distance to identify an application's most significant memory access patterns causing cache misses and provide insight into ways of improving data reuse. Unlike previous approaches, our tool combines (1) analysis and instrumentation of fully optimized binaries, (2) online analysis of reuse patterns, (3) fine-grain attribution of measurements and models to statements, loops and variables, and (4) static analysis of access patterns to quantify spatial reuse. We demonstrate the effectiveness of our approach for understanding reuse patterns in two scientific codes: one for simulating neutron transport and a second for simulating turbulent transport in burning plasmas. Our tools pinpointed opportunities for enhancing data reuse. Using this feedback as a guide, we transformed the codes, reducing their misses at various levels of the memory hierarchy by integer factors and reducing their execution time by as much as 60% and 33%, respectively.

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A general predictive performance model for wavefront algorithms on clusters of SMPs

Cited by 41 publications

References 4 publications

Scalable Analysis Techniques for Microprocessor Performance Counter Metrics

Scalable Analysis Techniques for Microprocessor Performance Counter Metrics

Out-of-Core Wavefront Computations with Reduced Synchronization

Pinpointing and Exploiting Opportunities for Enhancing Data Reuse

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