Research on Parallel Visualization in Large-Scale Scientific Computing

2006 ◽  
Author(s):  
Jiaquan Gao ◽  
Duanyang Zhao
Keyword(s):  
Large Scale ◽  
Scientific Computing ◽  
Parallel Visualization

Exploiting resource profiling mechanism for large-scale scientific computing on grids

2016 ◽  
Vol 19 (3) ◽  
pp. 1527-1539 ◽  
Author(s):  
Md. Azam Hossain ◽  
Cao Ngoc Nguyen ◽  
Jik-Soo Kim ◽  
Soonwook Hwang
Keyword(s):  
Large Scale ◽  
Scientific Computing

Cloud in Science

2014 ◽  
pp. 165-191
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Scientific Computing ◽  
Scientific Discovery ◽  
Software Environment ◽  
Access To Resources ◽  
On Demand ◽  
Service Models ◽  
New Models ◽  
The Impact

Cloud computing technologies and service models are attractive to scientific computing users due to the ability to get on-demand access to resources as well as the ability to control the software environment. Scientific computing researchers and resource providers servicing these users are considering the impact of new models and technologies. SaaS solutions like Globus Online and IaaS solutions such as Nimbus Infrastructure and OpenNebula accelerate the discovery of science by helping scientists to conduct advanced and large-scale science. This chapter describes how cloud is helping researchers to accelerate scientific discovery by transforming manual and difficult tasks into the cloud.

Parallel visualization of large-scale multifield scientific data

2019 ◽  
Vol 22 (6) ◽  
pp. 1107-1123
Author(s):  
Yi Cao ◽  
Zeyao Mo ◽  
Zhiwei Ai ◽  
Huawei Wang ◽  
Li Xiao ◽  
...  
Keyword(s):  
Large Scale ◽  
Scientific Data ◽  
Parallel Visualization

Approximate weighted matching on emerging manycore and multithreaded architectures

2012 ◽  
Vol 26 (4) ◽  
pp. 413-430 ◽  
Author(s):  
Mahantesh Halappanavar ◽  
John Feo ◽  
Oreste Villa ◽  
Antonino Tumeo ◽  
Alex Pothen
Keyword(s):  
Approximation Algorithms ◽  
Large Scale ◽  
Scientific Computing ◽  
Graph Matching ◽  
Combinatorial Problem ◽  
Fine Tuning ◽  
Matching Problem ◽  
Weighted Matching ◽  
Wide Range ◽  
The Impact

Graph matching is a prototypical combinatorial problem with many applications in high-performance scientific computing. Optimal algorithms for computing matchings are challenging to parallelize. Approximation algorithms are amenable to parallelization and are therefore important to compute matchings for large-scale problems. Approximation algorithms also generate nearly optimal solutions that are sufficient for many applications. In this paper we present multithreaded algorithms for computing half-approximate weighted matching on state-of-the-art multicore (Intel Nehalem and AMD Magny-Cours), manycore (Nvidia Tesla and Nvidia Fermi), and massively multithreaded (Cray XMT) platforms. We provide two implementations: the first uses shared work queues and is suited for all platforms; and the second implementation, based on dataflow principles, exploits special features available on the Cray XMT. Using a carefully chosen dataset that exhibits characteristics from a wide range of applications, we show scalable performance across different platforms. In particular, for one instance of the input, an R-MAT graph (RMAT-G), we show speedups of about [Formula: see text] on [Formula: see text] cores of an AMD Magny-Cours, [Formula: see text] on [Formula: see text] cores of Intel Nehalem, [Formula: see text] on Nvidia Tesla and [Formula: see text] on Nvidia Fermi relative to one core of Intel Nehalem, and [Formula: see text] on [Formula: see text] processors of Cray XMT. We demonstrate strong as well as weak scaling for graphs with up to a billion edges using up to 12,800 threads. We avoid excessive fine-tuning for each platform and retain the basic structure of the algorithm uniformly across platforms. An exception is the dataflow algorithm designed specifically for the Cray XMT. To the best of the authors' knowledge, this is the first such large-scale study of the half-approximate weighted matching problem on multithreaded platforms. Driven by the critical enabling role of combinatorial algorithms such as matching in scientific computing and the emergence of informatics applications, there is a growing demand to support irregular computations on current and future computing platforms. In this context, we evaluate the capability of emerging multithreaded platforms to tolerate latency induced by irregular memory access patterns, and to support fine-grained parallelism via light-weight synchronization mechanisms. By contrasting the architectural features of these platforms against the Cray XMT, which is specifically designed to support irregular memory-intensive applications, we delineate the impact of these choices on performance.

HIGH-PERFORMANCE PARALLEL VISUALIZATION LIBRARY FOR VARIOUS SIMULATION ENVIRONMENTS

2008 ◽  
Vol 05 (02) ◽  
pp. 273-287
Author(s):  
LI CHEN ◽  
HIROSHI OKUDA
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Unstructured Grids ◽  
Structured Grids ◽  
The Earth ◽  
Parallel Frameworks ◽  
Parallel Visualization ◽  
Simulation Environments

This paper describes a parallel visualization library for large-scale datasets developed in the HPC-MW project. Three parallel frameworks are provided in the library to satisfy different requirements of applications. Meanwhile, it is applicable for a variety of mesh types covering particles, structured grids and unstructured grids. Many techniques have been employed to improve the quality of the visualization. High speedup performance has been achieved by some hardware-oriented optimization strategies on different platforms, from PC clusters to the Earth Simulator. Good results have been obtained on some typical parallel platforms, thus demonstrating the feasibility and effectiveness of our library.

Sign in / Sign up

Export Citation Format

Share Document