In earlier times, computer systems had only a single core or processor. In these computers, the number of transistors on-chip (i.e. on the processor) doubled every two years and all applications enjoyed free speedup. Subsequently, with more and more transistors being packed on-chip, power consumption became an issue, frequency scaling reached its limits and industry […]

July 11, 2014 by hgpu

In this paper, we develop, study and implement a restricted additive Schwarz (RAS) preconditioner for speedup of the solution of sparse linear systems on NVIDIA Tesla GPU. A novel algorithm for constructing this preconditioner is proposed. This algorithm involves two phases. In the first phase, the construction of the RAS preconditioner is transformed to an […]

July 11, 2014 by hgpu

Linear systems are required to solve in many scientific applications and the solution of these systems often dominates the total running time. In this paper, we introduce our work on developing parallel linear solvers and preconditioners for solving large sparse linear systems using NVIDIA GPUs. We develop a new sparse matrix-vector multiplication kernel and a […]

July 11, 2014 by hgpu

The examination timetabling problem belongs to the class of combinatorial optimization problems and is of great importance for every University. In this paper, a hybrid evolutionary algorithm running on a GPU is employed to solve the examination timetabling problem. The hybrid evolutionary algorithm proposed has a genetic algorithm component and a greedy steepest descent component. […]

July 7, 2014 by hgpu

In this paper we present new hybrid CPU-GPU routines to accelerate the solution of linear systems, with band coefficient matrix, by off-loading the major part of the computations to the GPU and leveraging highly tuned implementations of the BLAS for the graphics processor. Our experiments with an nVidia S2070 GPU report speed-ups up to 6x […]

June 23, 2014 by hgpu

We present implementation details of a reordering strategy for permuting elements whose absolute value is large to the diagonal of a sparse matrix. This algorithm, based on work by Duff and Koster [9], is a critical component of the SPIKE-based preconditioner provided by the Spike::GPU library [2]. We discuss the four stages required to implement […]

June 1, 2014 by hgpu

Sparse matrix-vector multiplication (SMVM) is a crucial primitive used in a variety of scientific and commercial applications. Despite having significant parallelism, SMVM is a challenging kernel to optimize due to its irregular memory access characteristics. Numerous studies have proposed the use of FPGAs to accelerate SMVM implementations. However, most prior approaches focus on parallelizing multiply-accumulate […]

May 21, 2014 by hgpu

CP2K is an application for atomistic and molecular simulation and, with its excellent scalability, is particularly important with regards to use on future exascale systems. The code is well parallelized using MPI and hybrid MPI/OpenMP, typically scaling well to ~1 core per atom in the system. The research on CP2K done within PRACE-1IP stated that […]

May 16, 2014 by hgpu

Intel Xeon Phi is a coprocessor with sixty-one cores in a single chip. The chip has a more powerful FPU that contains 512-bit SIMD registers. Intel Xeon Phi chip can benefit from the algorithms that operate with the large vectors. In this work, sequential, multithreaded and distributed versions of SuperLU solvers are tested on the […]

May 2, 2014 by hgpu

The hardware and software evolutions related to Graphics Processing Units (GPUs), for general purpose computations, have changed the way the parallel programming issues are addressed. Many applications are being ported onto GPU for achieving performance gain. The GPU execution time is continuously optimized by the GPU programmers while optimizing pre-GPU computation overheads attracted the research […]

April 24, 2014 by hgpu

Numerical methods in sparse linear algebra typically rely on a fast and efficient matrix vector product, as this usually is the backbone of iterative algorithms for solving eigenvalue problems or linear systems. Against the background of a large diversity in the characteristics of high performance computer architectures, it is a challenge to derive a cross-platform […]

April 7, 2014 by hgpu

Krylov subspace solvers are often the method of choice when solving sparse linear systems iteratively. At the same time, hardware accelerators such as graphics processing units (GPUs) continue to offer significant floating point performance gains for matrix and vector computations through easy-to-use libraries of computational kernels. However, as these libraries are usually composed of a […]

April 6, 2014 by hgpu