high performance computing on graphics processing units: hgpu.org

hgpu.org » Heterogeneous systems

Iterative SLE Solvers over a CPU-GPU Platform

Alecio P.D. Binotto, Christian Daniel, Daniel Weber, Arjan Kuijper, Andre Stork, Carlos Pereira, Dieter Fellner

Tags: Algorithms, Fluid dynamics, Heterogeneous systems, Linear Algebra

May 14, 2011 by hgpu

Data-aware scheduling of legacy kernels on heterogeneous platforms with distributed memory

Michela Becchi, Surendra Byna, Srihari Cadambi, Srimat Chakradhar

Tags: Computer science, CUDA, Distributed data structures, Heterogeneous systems, nVidia, Performance, Programming Languages, Sparse matrix, Tesla C870

May 10, 2011 by hgpu

Programming Challenges for the Implementation of Numerical Quadrature in Atomic Physics on FPGA and GPU Accelerators

C.J. Gillan, T. Steinke, J. Bock, S. Borchert, I. Spence, N.S. Scott

Tags: FPGA, Heterogeneous systems, Physics, Quantum Physics

May 5, 2011 by hgpu

Optimized HPL for AMD GPU and multi-core CPU usage

Matthias Bach, Matthias Kretz, Volker Lindenstruth, David Rohr

Tags: ATI, ATI Radeon HD 5870, Computer science, GPU cluster, Heterogeneous systems, Linear Algebra, MPI, Performance

April 25, 2011 by hgpu

MITHRA: Multiple data independent tasks on a heterogeneous resource architecture

Reza Farivar, Abhishek Verma, Ellick M. Chan, Roy H. Campbell

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, MapReduce, nVidia, nVidia GeForce 9800 GX2, nVidia Quadro FX 570

April 22, 2011 by hgpu

Accelerating Linpack Performance with Mixed Precision Algorithm on CPU+GPGPU Heterogeneous Cluster

Wang Lei, Zhang Yunquan, Zhang Xianyi, Liu Fangfang

Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, Linear Algebra, Mixed precision, nVidia, nVidia GeForce GTX 295, Tesla C1060

April 20, 2011 by hgpu

HPP-Controller: An intra-node controller designed for connecting heterogeneous CPUs

Qiang Li, Panyong Zhang, Ninghui Sun

Tags: Cell processor, Computer science, FPGA, Heterogeneous systems, Memory, Performance

April 19, 2011 by hgpu

Single-Chip Heterogeneous Computing: Does the Future Include Custom Logic, FPGAs, and GPGPUs?

Eric S. Chung, Peter A. Milder, James C. Hoe, Ken Mai

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Tags: ASIC, ATI, ATI CAL, ATI Radeon HD 5870, Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Performance

April 19, 2011 by hgpu

Building a Personal High Performance Computer with Heterogeneous Processors

Qiang Li, Zhigang Huo, Ninghui Sun

Tags: Computer science, Heterogeneous systems, Performance

April 18, 2011 by hgpu

Exploiting Computational Resources in Distributed Heterogeneous Platforms

George Teodoro, Rafael Sachetto, Daniel Fireman, Dorgival Guedes, Renato Ferreira

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 260

April 16, 2011 by hgpu

Hybrid Map Task Scheduling for GPU-Based Heterogeneous Clusters

Koichi Shirahata, Hitoshi Sato, Satoshi Matsuoka

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Presentation, Task scheduling, Tesla S1070

April 16, 2011 by hgpu

Scaleable Sparse Matrix-Vector Multiplication with Functional Memory and GPUs

Noboru Tanabe, Yuuka Ogawa, Masami Takata, Kazuki Joe

Tags: Computer science, Heterogeneous systems, Matrix multiplication, Sparse matrix

April 14, 2011 by hgpu

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

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high performance computing on graphics processing units: hgpu.org

Iterative SLE Solvers over a CPU-GPU Platform

Data-aware scheduling of legacy kernels on heterogeneous platforms with distributed memory

Programming Challenges for the Implementation of Numerical Quadrature in Atomic Physics on FPGA and GPU Accelerators

Optimized HPL for AMD GPU and multi-core CPU usage

MITHRA: Multiple data independent tasks on a heterogeneous resource architecture

Accelerating Linpack Performance with Mixed Precision Algorithm on CPU+GPGPU Heterogeneous Cluster

HPP-Controller: An intra-node controller designed for connecting heterogeneous CPUs

Single-Chip Heterogeneous Computing: Does the Future Include Custom Logic, FPGAs, and GPGPUs?

Building a Personal High Performance Computer with Heterogeneous Processors

Exploiting Computational Resources in Distributed Heterogeneous Platforms

Hybrid Map Task Scheduling for GPU-Based Heterogeneous Clusters

Scaleable Sparse Matrix-Vector Multiplication with Functional Memory and GPUs

Recent source codes

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

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