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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Scalable Energy Efficiency with Resilience for High Performance Computing Systems: A Quantitative Methodology

Energy efficiency and resilience are two crucial challenges for HPC systems to reach exascale. While energy efficiency and resilience issues have been extensively studied individually, little has been done to understand the interplay between energy efficiency and resilience for HPC systems. Decreasing the supply voltage associated with a given operating frequency for processors and other CMOS-based components can significantly reduce power consumption. However, this often raises system failure rates and consequently increases application execution time. In this work, we present an energy saving undervolting approach that leverages the mainstream resilience techniques to tolerate the increased failures caused by undervolting.

energy, resilience↗
DOE OSTI2017

High Performance Computing Based Parallel HIearchical Modal Association Clustering (HPAR HMAC)

For many applications, clustering is a crucial step in order to gain insight into the makeup of a dataset. The best approach to a given problem often depends on a variety of factors, such as the size of the dataset, time restrictions, and soft clustering requirements. The HMAC algorithm seeks to combine the strengths of 2 particular clustering approaches: model-based and linkage-based clustering. One particular weakness of HMAC is its computational complexity. HMAC is not practical for mega-scale data clustering. For high-definition imagery, a user would have to wait months or years for a result; for a 16-megapixel image, the estimated runtime skyrockets to over a decade! To improve the execution time of HMAC, it is reasonable to consider an multi-core implementation that utilizes available system resources. An existing imple-mentation (Ray and Cheng 2014) divides the dataset into N partitions - one for each thread prior to executing the HMAC algorithm. This implementation benefits from 2 types of optimization: parallelization and divide-and-conquer. By running each partition in parallel, the program is able to accelerate computation by utilizing more system resources. Although the parallel implementation provides considerable improvement over the serial HMAC, it still suffers from poor computational complexity, O(N2). Once the maximum number of cores on a system is exhausted, the program exhibits slower behavior. We now consider a modification to HMAC that involves a recursive partitioning scheme. Our modification aims to exploit divide-and-conquer benefits seen by the parallel HMAC implementation. At each level in the recursion tree, partitions are divided into 2 sub-partitions until a threshold size is reached. When the partition can no longer be divided without falling below threshold size, the base HMAC algorithm is applied. This results in a significant speedup over the parallel HMAC.

Patlolla, Dilip R↗
DOE OSTI2016

Re-Form: FPGA-Powered True Codesign Flow for High-Performance Computing In The Post-Moore Era

Multicore scaling will end soon because of practical power limits. Dark silicon is becoming a major issue even more than the end of Moore’s law. In the post-Moore era, the energy efficiency of computing will be a major concern. FPGAs could be a key to maximizing the energy efficiency. In this paper we address severe challenges in the adoption of FPGA in HPC and describe “Re-form,” an FPGA-powered codesign flow.

Cappello, Franck↗
DOE OSTI2017

Distributed metadata in a high performance computing environment

A computer-executable method, system, and computer program product for managing meta-data in a distributed storage system, wherein the distributed storage system includes one or more burst buffers enabled to operate with a distributed key-value store, the co computer-executable method, system, and computer program product comprising receiving a request for meta-data associated with a block of data stored in a first burst buffer of the one or more burst buffers in the distributed storage system, wherein the meta data is associated with a key-value, determining which of the one or more burst buffers stores the requested metadata, and upon determination that a first burst buffer of the one or more burst buffers stores the requested metadata, locating the key-value in a portion of the distributed key-value store accessible from the first burst buffer.

97 MATHEMATICS AND COMPUTING↗