high performance computing on graphics processing units: hgpu.org

In the present article we describe the implementation of the finite element numerical integration algorithm for the Xeon Phi coprocessor. The coprocessor is an extension of the idea of the many-core specialized unit for calculations and, by assumption, its performance has to be competitive with the current families of GPUs. Its main advantage is the […]

This paper presents a novel extended dynamic programming approach for energy minimization (EDP) to solve the correspondence problem for stereo and motion. A significant speedup is achieved using a recursive minimum search strategy (RMS). The mentioned speedup is particularly important if the disparity space is 2D as well as 3D. The proposed RMS can also […]

Field Programmable Gate Arrays (FPGAs) are an ideal platform for building systems with custom hardware accelerators, however managing these systems is still a major challenge. The OpenCL standard has become accepted as a good programming model for managing heterogeneous platforms due to its rich constructs. Although commercial OpenCL frameworks are now emerging, there is a […]

This paper presents an implementation of different matrix-matrix multiplication routines in OpenCL. We utilize the high-performance GEMM (GEneral Matrix-Matrix Multiply) implementation from our previous work for the present implementation of other matrix-matrix multiply routines in Level-3 BLAS (Basic Linear Algebra Subprograms). The other routines include SYMM (Symmetric Matrix-Matrix Multiply), SYRK (Symmetric Rank-K Update), SYR2K (Symmetric […]

Solving linear inverse problems where the solution is known to be sparse is of interest to both signal processing and machine learning research. The standard algorithms for solving such problems are sequential in nature – they tend to be slow for large scale problems. In the past, researchers have used Graphics Processing Units to accelerate […]

The particle-mesh spreading operation maps a value at an arbitrary particle position to contributions at regular positions on a mesh. This operation is often used when a calculation involving irregular positions is to be performed in Fourier space. We study several approaches for particle mesh spreading on GPUs. A central concern is the use of […]

Today, heterogeneous computing has truly reshaped the way scientists think and approach high-performance computing (HPC). Hardware accelerators such as general-purpose graphics processing units (GPUs) and Intel Many Integrated Core (MIC) architecture continue to make in-roads in accelerating large-scale scientific applications. These advancements, however, introduce new sets of challenges to the scientific community such as: selection […]

We describe the neural-network training framework used in the Kaldi speech recognition toolkit, which is geared towards training DNNs with large amounts of training data using multiple GPU-equipped or multi-core machines. In order to be as hardware-agnostic as possible, we needed a way to use multiple machines without generating excessive network traffic. Our method is […]

Graphics Processing Units (GPUs) are highly parallel shared memory microprocessors, and as such, they are prone to the same concurrency considerations as their traditional multicore CPU counterparts. In this thesis, we consider shared memory consistency, i.e. what values can be read when issued concurrently with writes on current GPU hardware. While memory consistency has been […]

Capabilities of using Graphic Processing Units (GPU) as a computational tool in CFD have been investigated here. Several solvers for solving linear matrix equations have been benchmarked on GPU and is shown that Gauss-Seidle gives the best performance for the GPU architecture. Compared to CPU on a case of lid-driven cavity flow, speedups of up […]

This dissertation studies a graphical processing unit (GPU) construction of Bayesian neural networks (BNNs) using large training data sets. The goal is to create a program for the mapping of phenomenological Minimal Supersymmetric Standard Model (pMSSM) parameters to their predictions. This would allow for a more robust method of studying the Minimal Supersymmetric Standard Model, […]

The longest common subsequence (LCS) problem is one of the classic problems in string processing. It is commonly used in file comparison, pattern recognition, and computational biology as a measure of sequence similarity. Given a set of strings, the LCS is the longest string that is a subsequence of every string in the set. For […]