high performance computing on graphics processing units: hgpu.org

The conference will consider mathematical, computer science and engineering developments that impact on the use of HPC in engineering analysis, design, and simulation. Engineering is interpreted in its widest sense to include aeronautical, civil, mechanical, electrical, materials, bioengineering, geotechnical, structural and environmental fields. The range of topics considered by the Conference will include: The mathematical […]

This international conference in Shenzhen will focus on understanding the potential usage of GPU and MIC from a computational scientific user point of view, particularly for multiscale problems in science on engineering. It brings together experts from China, Japan, and bordering Pacific countries such as the USA, Korea，Australia and Singapore. In addition to algorithmic research, […]

Graphics Processing Units (GPUs) have been conventionally used in the acceleration of 2D, 3D graphics and video rendering. Because of its performance and capability, the GPU has evolved into a highly parallel programmable processor that specializes in memory bandwith utilization and intensive computation. For operations involving graphics, GPUs offer a lot of gigaflops of processing […]

In this report, we present our initial proposal on simulating computations on a restricted variant of Evolution-Communication P system with energy (ECPe system) which will then be implemented in Graphics Processing Units (GPUs). This ECPe systems variant prohibits the use of antiport rules for communication. Several possible levels of parallelizations for simulating ECPe systems computations […]

General purpose computation on graphics processing unit (GPU) is prominent in the high performance computing era of this time. Porting or accelerating the data parallel applications onto GPU gives the default performance improvement because of the increased computational units. Better performances can be seen if application specific fine tuning is done with respect to the […]

The paper aims to provide a comparitive analysis on the performance of in memory databases as opposed to a customised graph database written ground up whose joins(searches) are performed on a GPGPU. This is done primarily to serve as a proof of concept on how databases that are represented as graphs can benefit by fostering […]

This work presents a methodology that parallelizes the simulation of mixed-abstraction level SystemC models across multicore CPUs, and graphics processing units (GPUs) for improved simulation performance. Given a SystemC model, we partition it into processes suitable for GPU execution and CPU execution. We convert the processes identified for GPU execution into GPU kernels with additional […]

Traditional CPU is able to run only a few complex threads concurrently. On the other side, a GPU allows a concurrent execution of hundreds or thousands of simpler threads. The GPU was originally designed for a computer graphics, but nowadays it is being used for general-purpose calculations using a GPGPU technology. CUDA, one of the […]

The current trend in the high performance computing shows a dramatic increase in the number of cores on the shared memory compute nodes. Algorithms, especially those related to linear algebra, need to be adapted to these new computer architectures in order to be efficient. PASTIX is a sparse parallel direct solver, that incorporates a dynamic […]

Current CPU and GPU architectures heavily use data and instruction parallelism at different levels. Floating point operations are organised in vector instructions of increasing vector length. For reasons of performance it is mandatory to use the vector instructions efficiently. Several ways of tuning a model problem finite difference stencil computation are discussed. The combination of […]

Parallel programming is difficult. For regular computation on central processing units application programming interfaces such as OpenMP, which augment normal sequential programs with preprocessor directives to achieve parallelism, have proven to be easy for programmers and they provide good multithreaded performance. OpenACC is a fork of the OpenMP project, which aims to provide a similar […]

General purpose GPU (GPGPU) programming frameworks such as OpenCL and CUDA allow running individual computation kernels sequentially on a device. However, in some cases it is possible to utilize device resources more efficiently by running kernels concurrently. This raises questions about load balancing and resource allocation that have not previously warranted investigation. For example, what […]