scholarly journals Accelerating reaction–diffusion simulations with general-purpose graphics processing units

2010 ◽  
Vol 27 (2) ◽  
pp. 288-290 ◽  
Author(s):  
Matthias Vigelius ◽  
Aidan Lane ◽  
Bernd Meyer
Keyword(s):  
Graphics Processing Units ◽  
Reaction Diffusion ◽  
General Purpose ◽  
Graphics Processing

scholarly journals DSPSR: Digital Signal Processing Software for Pulsar Astronomy

2011 ◽  
Vol 28 (1) ◽  
pp. 1-14 ◽  
Author(s):  
W. van Straten ◽  
M. Bailes
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Digital Signal ◽  
General Purpose ◽  
Design Decisions ◽  
Extensive Range ◽  
Processing Software ◽  
Graphics Processing

Abstractdspsr is a high-performance, open-source, object-oriented, digital signal processing software library and application suite for use in radio pulsar astronomy. Written primarily in C++, the library implements an extensive range of modular algorithms that can optionally exploit both multiple-core processors and general-purpose graphics processing units. After over a decade of research and development, dspsr is now stable and in widespread use in the community. This paper presents a detailed description of its functionality, justification of major design decisions, analysis of phase-coherent dispersion removal algorithms, and demonstration of performance on some contemporary microprocessor architectures.

scholarly journals A Representation of Membrane Computing with a Clustering Algorithm on the Graphical Processing Unit

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1199
Author(s):  
Ravie Chandren Muniyandi ◽  
Ali Maroosi
Keyword(s):  
Graphics Processing Units ◽  
Clustering Algorithm ◽  
Hamiltonian Path ◽  
Fold Increase ◽  
General Purpose ◽  
Processing Unit ◽  
Thread Block ◽  
Hard Problems ◽  
Graphical Processing ◽  
Graphics Processing

Long-timescale simulations of biological processes such as photosynthesis or attempts to solve NP-hard problems such as traveling salesman, knapsack, Hamiltonian path, and satisfiability using membrane systems without appropriate parallelization can take hours or days. Graphics processing units (GPU) deliver an immensely parallel mechanism to compute general-purpose computations. Previous studies mapped one membrane to one thread block on GPU. This is disadvantageous given that when the quantity of objects for each membrane is small, the quantity of active thread will also be small, thereby decreasing performance. While each membrane is designated to one thread block, the communication between thread blocks is needed for executing the communication between membranes. Communication between thread blocks is a time-consuming process. Previous approaches have also not addressed the issue of GPU occupancy. This study presents a classification algorithm to manage dependent objects and membranes based on the communication rate associated with the defined weighted network and assign them to sub-matrices. Thus, dependent objects and membranes are allocated to the same threads and thread blocks, thereby decreasing communication between threads and thread blocks and allowing GPUs to maintain the highest occupancy possible. The experimental results indicate that for 48 objects per membrane, the algorithm facilitates a 93-fold increase in processing speed compared to a 1.6-fold increase with previous algorithms.

Accelerating in-memory transaction processing using general purpose graphics processing units

2019 ◽  
Vol 97 ◽  
pp. 836-848
Author(s):  
Lan Gao ◽  
Yunlong Xu ◽  
Rui Wang ◽  
Hailong Yang ◽  
Zhongzhi Luan ◽  
...  
Keyword(s):  
Graphics Processing Units ◽  
Transaction Processing ◽  
General Purpose ◽  
Graphics Processing

scholarly journals Accelerated FDPS: Algorithms to use accelerators with FDPS

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Masaki Iwasawa ◽  
Daisuke Namekata ◽  
Keigo Nitadori ◽  
Kentaro Nomura ◽  
Long Wang ◽  
...  
Keyword(s):  
Graphics Processing Units ◽  
High Performance ◽  
General Purpose ◽  
Performance Model ◽  
Performance Tuning ◽  
Data Types ◽  
Interaction Function ◽  
Current Implementation ◽  
And Performance ◽  
Graphics Processing

Abstract We describe algorithms implemented in FDPS (Framework for Developing Particle Simulators) to make efficient use of accelerator hardware such as GPGPUs (general-purpose computing on graphics processing units). We have developed FDPS to make it possible for researchers to develop their own high-performance parallel particle-based simulation programs without spending large amounts of time on parallelization and performance tuning. FDPS provides a high-performance implementation of parallel algorithms for particle-based simulations in a “generic” form, so that researchers can define their own particle data structure and interparticle interaction functions. FDPS compiled with user-supplied data types and interaction functions provides all the necessary functions for parallelization, and researchers can thus write their programs as though they are writing simple non-parallel code. It has previously been possible to use accelerators with FDPS by writing an interaction function that uses the accelerator. However, the efficiency was limited by the latency and bandwidth of communication between the CPU and the accelerator, and also by the mismatch between the available degree of parallelism of the interaction function and that of the hardware parallelism. We have modified the interface of the user-provided interaction functions so that accelerators are more efficiently used. We also implemented new techniques which reduce the amount of work on the CPU side and the amount of communication between CPU and accelerators. We have measured the performance of N-body simulations on a system with an NVIDIA Volta GPGPU using FDPS and the achieved performance is around 27% of the theoretical peak limit. We have constructed a detailed performance model, and found that the current implementation can achieve good performance on systems with much smaller memory and communication bandwidth. Thus, our implementation will be applicable to future generations of accelerator system.

scholarly journals Special issue on evolutionary computation on general purpose graphics processing units

2011 ◽  
Vol 16 (2) ◽  
pp. 185-186 ◽  
Author(s):  
José L. Risco-Martín ◽  
Juan Lanchares ◽  
Carlos A. Coello-Coello
Keyword(s):  
Evolutionary Computation ◽  
Graphics Processing Units ◽  
General Purpose ◽  
Special Issue ◽  
Graphics Processing

Reducing Power of Memory Hierarchy in General Purpose Graphics Processing Units

2017 ◽  
Vol 13 (2) ◽  
pp. 149-165
Author(s):  
Ahsan Saghir ◽  
Ehsan Atoofian ◽  
Ali Manzak
Keyword(s):  
Graphics Processing Units ◽  
Memory Hierarchy ◽  
Reducing Power ◽  
General Purpose ◽  
Graphics Processing

scholarly journals Acceleration of ray tracing method using predictive evaluation and GPGPU technology

2014 ◽  
Vol 4 (3) ◽  
Author(s):  
Branislav Sobota ◽  
Štefan Korečko ◽  
Csaba Szabó ◽  
František Hrozek
Keyword(s):  
Ray Tracing ◽  
Graphics Processing Units ◽  
Computation Time ◽  
General Purpose ◽  
Ray Tracing Method ◽  
Parallel Solution ◽  
Graphics Processing ◽  
Tracing Method ◽  
Selection Of ◽  
Parallel Ray Tracing

AbstractRay tracing is one of computer graphics methods for achieving the most realistic outputs. Its main disadvantage is high computation demands. Removal of this disadvantage is possible using parallelization due to the fact that the ray tracing method is inherently parallel. Solution presented in this article uses GPGPU (general-purpose computing on graphics processing units) technology and a predictive evaluation for the acceleration of ray tracing method. The CUDA C was selected as a GPGPU language and it was used for a conversion of a raytracer core. The main reason for choosing this language was usage of the Tesla C1060 graphics card. The predictive evaluation of a scene was based on the fact that total computation time increases proportionally with resolution. This evaluation allows selection of the optimal scene division for the parallel ray tracing. In tests, proposed GPGPU solution reached accelerations up to 28.3× comparing to CPU.

Sign in / Sign up

Export Citation Format

Share Document