High Performance Computing for Analyzing PB-Scale Data in Nuclear Experiments and Simulations

2011 ◽  
pp. 107-117
Author(s):  
Takayuki Tatekawa ◽  
Naoya Teshima ◽  
Noriyuki Kushida ◽  
Hiroko Nakamura Miyamura ◽  
Guehee Kim ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Performance Computing ◽  
Scale Data

scholarly journals SW-LZMA: Parallel Implementation of LZMA Based on SW26010 Many-Core Processor

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bingzheng Li ◽  
Jinchen Xu ◽  
Zijing Liu
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Cluster Systems ◽  
Large Scale Data ◽  
Many Core ◽  
High Performance Computing Cluster ◽  
Performance Computing ◽  
Scale Data ◽  
Computing Cluster

With the development of high-performance computing and big data applications, the scale of data transmitted, stored, and processed by high-performance computing cluster systems is increasing explosively. Efficient compression of large-scale data and reducing the space required for data storage and transmission is one of the keys to improving the performance of high-performance computing cluster systems. In this paper, we present SW-LZMA, a parallel design and optimization of LZMA based on the Sunway 26010 heterogeneous many-core processor. Combined with the characteristics of SW26010 processors, we analyse the storage space requirements, memory access characteristics, and hotspot functions of the LZMA algorithm and implement the thread-level parallelism of the LZMA algorithm based on Athread interface. Furthermore, we make a fine-grained layout of LDM address space to achieve DMA double buffer cyclic sliding window algorithm, which optimizes the performance of SW-LZMA. The experimental results show that compared with the serial baseline implementation of LZMA, the parallel LZMA algorithm obtains a maximum speedup ratio of 4.1 times using the Silesia corpus benchmark, while on the large-scale data set, speedup is 5.3 times.

scholarly journals Review: High-performance computing to detect epistasis in genome scale data sets

2015 ◽  
Vol 17 (3) ◽  
pp. 368-379 ◽  
Author(s):  
Alex Upton ◽  
Oswaldo Trelles ◽  
José Antonio Cornejo-García ◽  
James Richard Perkins
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Data Sets ◽  
Genome Scale ◽  
Performance Computing ◽  
Scale Data

The Recent Revolution in High Performance Computing

MRS Bulletin ◽  
1997 ◽  
Vol 22 (10) ◽  
pp. 5-6
Author(s):  
Horst D. Simon
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
New Technologies ◽  
New Technology ◽  
Parallel Architecture ◽  
Time Frame ◽  
Good News ◽  
Computing Industry ◽  
Time And Energy ◽  
Performance Computing

Recent events in the high-performance computing industry have concerned scientists and the general public regarding a crisis or a lack of leadership in the field. That concern is understandable considering the industry's history from 1993 to 1996. Cray Research, the historic leader in supercomputing technology, was unable to survive financially as an independent company and was acquired by Silicon Graphics. Two ambitious new companies that introduced new technologies in the late 1980s and early 1990s—Thinking Machines and Kendall Square Research—were commercial failures and went out of business. And Intel, which introduced its Paragon supercomputer in 1994, discontinued production only two years later.During the same time frame, scientists who had finished the laborious task of writing scientific codes to run on vector parallel supercomputers learned that those codes would have to be rewritten if they were to run on the next-generation, highly parallel architecture. Scientists who are not yet involved in high-performance computing are understandably hesitant about committing their time and energy to such an apparently unstable enterprise.However, beneath the commercial chaos of the last several years, a technological revolution has been occurring. The good news is that the revolution is over, leading to five to ten years of predictable stability, steady improvements in system performance, and increased productivity for scientific applications. It is time for scientists who were sitting on the fence to jump in and reap the benefits of the new technology.

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