scholarly journals Visualizing a Supercomputer: a Case of Objects Regrouping

2019 ◽  
Author(s):  
Владимир Авербух ◽  
Vladimir Averbukh ◽  
Александр Берсенев ◽  
Alexander Bersenev ◽  
Маджид Форгани ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Original Problem ◽  
Point Of View ◽  
Additional Information ◽  
Performance Visualization ◽  
Computing Performance ◽  
Successful Change ◽  
Performance Computing ◽  
Extra Structure

In the paper we present the situation which had required visualization of a large amount of non-trivial objects, such as supercomputer’s tasks. The method of visualization of these objects was hard to find. Then we used additional information about an extra structure on those objects. This knowledge led us to an idea of grouping the objects into new generalized ones. Those new artificial objects were easy to visualize due to their small quantity. And they happened to be enough for the cognition of the original problem. That was a successful change of point of view. As a whole, our work belongs to a high-performance computing performance visualization area. It gains valuable attention from scientists over the whole world, for example [1-2].

Comparison Between Experimental, Analytical and Simulation Model of Distributed Computation on ARM processors in High-Performance Computing

2017 ◽  
Vol 0 (0) ◽  
pp. 5-10 ◽  
Author(s):  
Paweł Augustynowicz ◽  
Aneta Buraczyńska
Keyword(s):  
Simulation Model ◽  
High Performance Computing ◽  
High Performance ◽  
Reference Model ◽  
Simulation Models ◽  
Distributed Computation ◽  
Point Of View ◽  
Raspberry Pi ◽  
Construction Process ◽  
Performance Computing

The paper presents a comparison between experimental, analytical and simulation model of distributed cryptographic computation regarding password recovery with SHA 1 password hashing. The aim of this paper is compare popular mobile ARM processors with their Intel Atom analogue and determine their usefulness in cryptographic computations from High Performance Computing (HPC) point of view. During the construction process of HPC cluster, three different versions of Raspberry Pi computers were used. Then the constructed model was applied to develop an analytical and simulation models that allow calculating most influential characteristics from HPC clusters administrator’s point of view. Reference model was constructed on Intel Atom processors.

HIGH-PERFORMANCE MATHEMATICAL FUNCTIONS FOR SINGLE-CORE ARCHITECTURES

2014 ◽  
Vol 23 (04) ◽  
pp. 1450051 ◽  
Author(s):  
MD. HAIDAR SHARIF
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Daily Life ◽  
Optimization Techniques ◽  
Trigonometric Functions ◽  
Mathematical Functions ◽  
Engineering Problems ◽  
Computing Performance ◽  
Execution Pattern ◽  
Performance Computing

Nowadays high-performance computing (HPC) architectures are designed to resolve assorted sophisticated scientific as well as engineering problems across an ever intensifying number of HPC and professional workloads. Application and computation of key trigonometric functions sine and cosine are in all spheres of our daily life, yet fairly time consuming task in high-performance numerical simulations. In this paper, we have delivered a detailed deliberation of how the micro-architecture of single-core Itanium® and Alpha 21264/21364 processors as well as the manual optimization techniques improve the computing performance of several mathematical functions. On describing the detailed algorithm and its execution pattern on the processor, we have confirmed that the processor micro-architecture side by side manual optimization techniques ameliorate computing performance significantly as compared to not only the standard math library's built-in functions with compiler optimizing options but also Intel® Itanium® library's highly optimized mathematical functions.

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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