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Jun, 23

IA-SpGEMM: An Input-aware Auto-tuning Framework for Parallel Sparse Matrix-Matrix Multiplication

Sparse matrix-matrix multiplication (SpGEMM) is a sparse kernel that is used in a number of scientific applications. Although several SpGEMM algorithms have been proposed, almost all of them are restricted to the compressed sparse row (CSR) format, and the possible performance gain from exploiting other formats has not been well studied. The particular format and […]
Apr, 25

Optimized HPL for AMD GPU and multi-core CPU usage

The installation of the LOEWE-CSC (http://csc.uni-frankfurt.de/csc/?51) supercomputer at the Goethe University in Frankfurt lead to the development of a Linpack which can fully utilize the installed AMD Cypress GPUs. At its core, a fast DGEMM for combined GPU and CPU usage was created. The DGEMM library is tuned to hide all DMA transfer times and […]
Aug, 19

Kernel Tuner: A search-optimizing GPU code auto-tuner

A very common problem in GPU programming is that some combination of thread block dimensions and other code optimization parameters, like tiling or unrolling factors, results in dramatically better performance than other kernel configurations. To obtain highly-efficient kernels it is often required to search vast and discontinuous search spaces that consist of all possible combinations […]
Jun, 21

cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL

This paper presents cltorch, a hardware-agnostic backend for the Torch neural network framework. cltorch enables training of deep neural networks on GPUs from diverse hardware vendors, including AMD, NVIDIA, and Intel. cltorch contains sufficient implementation to run models such as AlexNet, VGG, Overfeat, and GoogleNet. It is written using the OpenCL language, a portable compute […]
Feb, 16

Parallel and Scalable Sparse Basic Linear Algebra Subprograms

Sparse basic linear algebra subprograms (BLAS) are fundamental building blocks for numerous scientific computations and graph applications. Compared with Dense BLAS, parallelization of Sparse BLAS routines entails extra challenges due to the irregularity of sparse data structures. This thesis proposes new fundamental algorithms and data structures that accelerate Sparse BLAS routines on modern massively parallel […]
Sep, 17

CLTune: A Generic Auto-Tuner for OpenCL Kernels

This work presents CLTune, an auto-tuner for OpenCL kernels. It evaluates and tunes kernel performance of a generic, user-defined search space of possible parametervalue combinations. Example parameters include the OpenCL workgroup size, vector data-types, tile sizes, and loop unrolling factors. CLTune can be used in the following scenarios: 1) when there are too many tunable […]
Oct, 29

Implementing Level-3 BLAS Routines in OpenCL on Different Processing Units

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 […]
May, 11

A portable and high-performance matrix operations library for CPUs, GPUs and beyond

High-performance computing systems today include a variety of compute devices such as multi-core CPUs, GPUs and many-core accelerators. OpenCL allows programming different types of compute devices using a single API and kernel language. However, there is no standard matrix operations library in OpenCL for operations such as matrix multiplication that works well on a variety […]
Mar, 26

Improving Performance Portability in OpenCL Programs

We study the performance portability of OpenCL across diverse architectures including NVIDIA GPU, Intel Ivy Bridge CPU, and AMD Fusion APU. We present detailed performance analysis at assembly level on three exemplar OpenCL benchmarks: SGEMM, SpMV, and FFT. We also identify a number of tuning knobs that are critical to performance portability, including threads-data mapping, […]
Jul, 15

Implementing a Code Generator for Fast Matrix Multiplication in OpenCL on the GPU

This paper presents results of an implementation of code generator for fast general matrix multiply (GEMM) kernels. When a set of parameters is given, the code generator produces the corresponding GEMM kernel written in OpenCL. The produced kernels are optimized for high-performance implementation on GPUs from AMD. Access latencies to GPU global memory is the […]
Jun, 4

A Scalable High Performant Cholesky Factorization for Multicore with GPU Accelerators

We present a Cholesky factorization for multicore with GPU accelerators systems. The challenges in developing scalable high performance algorithms for these emerging systems stem from their heterogeneity, massive parallelism, and the huge gap between the GPUs’ compute power vs the CPU-GPU communication speed. We show an approach that is largely based on software infrastructures that […]

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