AJ Guillon
Today servers, desktops, mobile devices, and embedded systems contain many processors in addition to the CPU that runs programs. These extra processors are generally called accelerators and could be a GPU, FPGA, Xeon Phi, or other programmable device. There are many types of accelerators available, from many vendors, for many different environments. Khronos developed the […]
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Benedict R. Gaster
A popular approach to programming manycore GPUs is the Single Instruction Multiple Thread (SIMT) abstraction. SIMT has the benefit of presenting a "single thread" view, alleviating the complexity of explicitly vectorizing the source code. However, due to the SIMD nature of the underlying hardware it is often difficult to fully hide all aspects from the […]
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Kato Mivule, Benjamin Harvey, Crystal Cobb, Hoda El Sayed
The advent of high performance computing (HPC) and graphics processing units (GPU), present an enormous computation resource for Large data transactions (big data) that require parallel processing for robust and prompt data analysis. While a number of HPC frameworks have been proposed, parallel programming models present a number of challenges, for instance, how to fully […]
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Alastair F. Donaldson
I present a tutorial overview demonstrating the key technique used by GPUVerify, a static verification tool for graphics processing unit (GPU) kernels. The technique is a method for translating a massively parallel GPU kernel into a sequential program such that correctness of the sequential program implies data race-freedom of the parallel kernel.
Rashmi Sharan Sinha, Satvir Singh
In this paper, we present a comprehensive survey on parallelizing computations involved in optimization problem, on GPU using CUDA. Many researchers have reported significant speedup using CUDA on GPU. Stochastic algorithms, Metaheuristic algorithms and Heuristic algorithms i.e., Mixed Integer Non-linear Programming (MINLP), Central Force Optimization (CFO), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), etc. are […]
Filipo Novo Mor, Plauto de Abreu Neto
In this paper we describe the architecture of a NVIDIA GPU, as well as the CUDA programming model. The basic statements are explained. We also provide an example of CUDA code, explaining its execution workflow in a GPU device.
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Alexey Boreskov, Evgeniy Shikin
Computer Graphics: From Pixels to Programmable Graphics Hardware explores all major areas of modern computer graphics, starting from basic mathematics and algorithms and concluding with OpenGL and real-time graphics. It gives students a firm foundation in today’s high-performance graphics. UP-TO-DATE TECHNIQUES, ALGORITHMS, AND API: The book includes mathematical background on vectors and matrices as well […]
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Stian Aaraas Pedersen
Physically based rendering using ray tracing is capable of producing realistic images of much higher quality than other methods. However, the computational costs associated with exploring all paths of light are huge; it can take hours to render high quality images of complex scenes. Using graphics processing units has emerged as a popular way to […]
Rafael Keller Tesser, Philippe O. A. Navaux
The ability to predict the performance of applications in large-scale parallel systems is essential. One of the main incentives for this is the high cost of executing non-production tasks on these systems. An entity may also want to predict the performance in a system that does not yet exist. One popular alternative for increasing a […]
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Nicholas Wilt
The CUDA Handbook begins where CUDA by Example (Addison-Wesley, 2011) leaves off, discussing CUDA hardware and software in greater detail and covering both CUDA 5.0 and Kepler. Every CUDA developer, from the casual to the most sophisticated, will find something here of interest and immediate usefulness. Newer CUDA developers will see how the hardware processes […]
Shrikant Gond, Akshay Patil, V. B. Nikam
This paper contains the overview of various parallelization techniques to improve the performance of existing data mining algorithms and make the capable of handling large amount of data. There are variety of techniques to achieve the parallelization in data mining field, in this paper a brief introduction to few of the popular techniques is presented. […]
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Rakesh Kumar K. N, Hemalatha V, Shivakumar K. M, Basappa B. Kodada
In the last few years, emergence of High-Performance Computing has largely influenced computer technology in the field of financial analytics, data mining, image/signal processing, simulations and modeling etc. Multi-threading, hyper-threading and other parallel programming technologies, multicore machines, clusters etc. have helped achieve high performance with high availability and high throughput. However, hybrid clusters have been […]
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Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

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  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
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  • RAM: 16GB
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