Posts
Jul, 4
GPUvm: Why Not Virtualizing GPUs at the Hypervisor?
Graphics processing units (GPUs) provide orders-of-magnitude speedup for compute-intensive data-parallel applications. However, enterprise and cloud computing domains, where resource isolation of multiple clients is required, have poor access to GPU technology. This is due to lack of operating system (OS) support for virtualizing GPUs in a reliable manner. To make GPUs more mature system citizens, […]
Jul, 4
A Road Marking Extraction Method Using GPGPU
In driving assistance system (DAS), road marking’s data can provide important assistance for driving safety. As the input image usually includes unnecessary information, lane detection system usually needs to remove most unnecessary data except for the lane markings. In this paper, a road marking extraction method is proposed to separate the painted lane lines using […]
Jul, 3
Exploiting parallel features of modern computer architectures in bioinformatics: applications to genetics, structure comparison and large graph analysis
The exponential growth in bioinformatics data generation and the stagnation of processor frequencies in modern processors stress the need for efficient implementations that fully exploit the parallel capabilities offered by modern computers. This thesis focuses on parallel algorithms and implementations for bioinformatics problems. Various types of parallelism are described and exploited. This thesis presents applications […]
Jul, 3
The Framework and Compilation Techniques for Directive-based GPU Cluster Programming
GPU cluster is an important architecture being used for large scientific and engineering applications. However, manually developed GPU cluster application is still a very difficult task. To alleviate this problem, we adopt the OpenACC standard for directive-based approach and proposed some extension to support GPU cluster programming. The extensions are constructs and clauses used to […]
Jul, 3
Historic Learning Approach for Auto-tuning OpenACC Accelerated Scientific Applications
The performance optimization of scientific applications usually requires an in-depth knowledge of the hardware and software. A performance tuning mechanism is suggested to automatically tune OpenACC parameters to adapt to the execution environment on a given system. A historic learning based methodology is suggested to prune the parameter search space for a more efficient auto-tuning […]
Jul, 3
Reducing the Code Degree Of Parallelism to Increase GPUs Reliability
A higher Degree of Parallelism decreases the code execution time. However, to manage the increased number of parallel processes a higher scheduling strain is required and caches, registers, and other resources utilization will be affected. All these parallelism management variations may have the countermeasure of increasing the GPU neutron sensitivity. The results of an extensive […]
Jul, 3
Toward Auto-tuned Krylov Basis Computations with minimized Communication on Clusters of Accelerators
Krylov Subspace Methods (KSMs) are widely used for solving large scale linear systems and eigenproblems. However, the computing of Krylov subspace basis for KSMs suffers from its intensive blocking scalar product computation and communication, especially in large clusters with accelerators like GPUs. In this paper, a Hyper Graph based communication optimization is applied to Arnoldi […]
Jul, 1
Mixed-precision Orthogonalization Scheme and Adaptive Step Size for CA-GMRES on GPUs
We propose a mixed-precision orthogonalization scheme that takes the input matrix in a standard 32 or 64-bit floating-point precision, but accumulates its intermediate results in the doubled-precision. For a 64-bit input matrix, we use software emulation for the higher-precision arithmetics. Compared with the standard orthogonalization scheme, we require about 8:5 more computation but a much […]
Jul, 1
Energy Efficiency Benefits of Reducing the Voltage Guardband on the Kepler GPU Architecture
Energy efficiency of GPU architectures has emerged as an important design criterion for both NVIDIA and AMD. In this paper, we explore the benefits of scaling a general-purpose GPU (GPGPU) core’s supply voltage to the near limits of execution failure. We find that as much as 21% of NVIDIA GTX 680’s core supply voltage guardband […]
Jul, 1
Accelerated Computation of Minimum Enclosing Balls by GPU Parallelization and Distance Filtering
Minimum enclosing balls are used extensively to speed up multidimensional data processing in, e.g., machine learning, spatial databases, and computer graphics. We present a case study of several acceleration techniques that are applicable in enclosing ball algorithms based on repeated farthest-point queries. Parallel GPU solutions using CUDA are developed for both low- and high-dimensional cases. […]
Jul, 1
Parallelizing the cellular potts model on GPU and multi-core CPU: An OpenCL cross-platform study
In this paper, we present the analysis and development of a cross-platform OpenCL parallelization of the Cellular Potts Model (CPM). In general, the evolution of the CPM is time-consuming. Using data-parallel programming model such as CUDA can accelerate the process, but it is highly dependent on the hardware type and manufacturer. Recently, OpenCL has attracted […]
Jul, 1
High-Level Programming Framework for Executing Streaming Applications on Heterogeneous OpenCL Platforms
As the computer industry is reaching more and more limits regarding processor speed and transistor size, they have to come up with complex new architectures and more efficient use of the available processing power. For application developers this can be a difficult task, because they have to be aware of low-level hardware properties and there […]
