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Rajan, Mahesh

Publications and source records attributed to Rajan, Mahesh.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Application of Performance Analysis Tools on SNL ASC Codes

This milestone 1) exercised a broad set of performance profiling and analysis tools, including tools whose development has been promoted by the ASC program; 2) exercised the tools on two different SNL ASC codes, one Sierra code (Sierra/Aria, a C++ codebase) and one RAMSES code (ITS, a Fortran codebase); and 3) exercised the tools on multiple platforms, including the CTS-1 (e.g., Serrano) and ATS-1 Trinity (e.g., Mutrino) platforms. The milestone generated a plethora of strong and weak scaling, trend and profile data for multiple versions and problem cases for each of the two codes. A wealth of experience was gained with the various tools that included identification of problems, an improved understanding of feature sets, enhanced usage documentation, and insights for future tool-development. Results are provided from a large number and variety of performance analysis runs with the target codes, together with instructions for how to make use of the tools with the codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Continuous whole-system monitoring toward rapid understanding of production HPC applications and systems

A detailed understanding of HPC applications’ resource needs and their complex interactions with each other and HPC platform resources are critical to achieving scalability and performance. Such understanding has been difficult to achieve because typical application profiling tools do not capture the behaviors of codes under the potentially wide spectrum of actual production conditions and because typical monitoring tools do not capture system resource usage information with high enough fidelity to gain sufficient insight into application performance and demands. In this paper we present both system and application profiling results based on data obtained through synchronized system wide monitoring on a production HPC cluster at Sandia National Laboratories (SNL). We demonstrate analytic and visualization techniques that we are using to characterize application and system resource usage under production conditions for better understanding of application resource needs. Furthermore, our goals are to improve application performance (through understanding application-to-resource mapping and system throughput) and to ensure that future system capabilities match their intended workloads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Performance Efficiency and Effectivness of Supercomputers

Our first purpose here is to offer to a general technical and policy audience a perspective on whether the supercomputing community should focus on improving the efficiency of supercomputing systems and their use rather than on building larger and ostensibly more capable systems that are used at low efficiency. After first summarizing our content and defining some necessary terms, we give a concise answer to this question. We then set this in context by characterizing performance of current supercomputing systems on a variety of benchmark problems and actual problems drawn from workloads in the national security, industrial, and scientific context. Along the way we answer some related questions, identify some important technological trends, and offer a perspective on the significance of these trends. Our second purpose is to give a reasonably broad and transparent overview of the related issue space and thereby to better equip the reader to evaluate commentary and controversy concerning supercomputing performance. For example, questions repeatedly arise concerning the Linpack benchmark and its predictive power, so we consider this in moderate depth as an example. We also characterize benchmark and application performance for scientific and engineering use of supercomputers and offer some guidance on how to think about these. Examples here are drawn from traditional scientific computing. Other problem domains, for example, data analytics, have different performance characteristics that are better captured by different benchmark problems or applications, but the story in those domains is similar in character and leads to similar conclusions with regard to the motivating question.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗