high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

Benchmarking the Intel Xeon Phi Coprocessor

F. Masci

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Performance

February 11, 2014 by hgpu

Accurate Cross-Architecture Performance Modeling for Sparse Matrix-Vector Multiplication (SpMV) on GPUs

Ping Guo, Liqiang Wang

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Performance, Sparse matrix, Tesla C2050, Tesla K20, Tesla M2090

February 11, 2014 by hgpu

A Scalable Multi-Path Microarchitecture for Efficient GPU Control Flow

Ahmed ElTantawy, Jessica Wenjie Ma, Mike O'Connor, Tor M. Aamodt

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Tags: Computer science, Hardware Architecture, Performance

February 9, 2014 by hgpu

Strategies for Maximizing Utilization in multi-CPU & multi-GPU Heterogeneous Architectures

Angeles Navarro, Antonio Vilches, Francisco Corbera, Rafael Asenjo

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla S2050

February 4, 2014 by hgpu

Comparing the Performance of Different x86 SIMD Instruction Sets for a Medical Imaging Application on Modern Multi- and Manycore Chips

Johannes Hofmann, Jan Treibig, Georg Hager, Gerhard Wellein

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Tags: Algorithms, Computed tomography, Computer science, CT, CUDA, Image reconstruction, Intel Xeon Phi, Medicine, nVidia, nVidia GeForce GTX 680, Performance

January 30, 2014 by hgpu

Autotuning Programs with Algorithmic Choice

Jason Ansel

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Tags: Algorithms, ATI, ATI Radeon HD 6630 M, Computer science, nVidia, OpenCL, Package, Performance, Tesla C2070, Thesis

January 28, 2014 by hgpu

Improvement of the fused CUDA kernels performance prediction

Peter Novak

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Performance, Thesis

January 25, 2014 by hgpu

clpeak – peak performance of your opencl device

Krishnaraj Bhat

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Tags: ATI, Benchmarking, Computer science, nVidia, OpenCL, Package, Performance

January 23, 2014 by hgpu

Improving the Performance of CA-GMRES on Multicores with Multiple GPUs

Ichitaro Yamazaki, Hartwig Anzt, Stanimire Tomov, Mark Hoemmenx, Jack Dongarra

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Performance, Tesla M2090

January 18, 2014 by hgpu

Performance Engineering for a Medical Imaging Application on the Intel Xeon Phi Accelerator

Johannes Hofmann, Jan Treibig, Georg Hager, Gerhard Wellein

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Tags: Benchmarking, Computed tomography, Computer science, CT, Image reconstruction, Intel Xeon Phi, Medicine, Performance

January 17, 2014 by hgpu

MRPB: Memory Request Prioritization for Massively Parallel Processors

Wenhao Jia, Kelly A. Shaw, Margaret Martonosi

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, Performance, Tesla C2050

January 16, 2014 by hgpu

Towards Portable Performance for Explicit Hydrodynamics Codes

A. C. Mallinson, D. A. Beckingsale, W. P. Gaudin, J. A. Herdman, S. A. Jarvis

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Tags: APU, ATI, ATI FirePro V7800, Computer science, Fluid dynamics, MPI, nVidia, OpenCL, Package, Performance, Tesla K20

January 14, 2014 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

Benchmarking the Intel Xeon Phi Coprocessor

Accurate Cross-Architecture Performance Modeling for Sparse Matrix-Vector Multiplication (SpMV) on GPUs

A Scalable Multi-Path Microarchitecture for Efficient GPU Control Flow

Strategies for Maximizing Utilization in multi-CPU & multi-GPU Heterogeneous Architectures

Comparing the Performance of Different x86 SIMD Instruction Sets for a Medical Imaging Application on Modern Multi- and Manycore Chips

Autotuning Programs with Algorithmic Choice

Improvement of the fused CUDA kernels performance prediction

clpeak – peak performance of your opencl device

Improving the Performance of CA-GMRES on Multicores with Multiple GPUs

Performance Engineering for a Medical Imaging Application on the Intel Xeon Phi Accelerator

MRPB: Memory Request Prioritization for Massively Parallel Processors

Towards Portable Performance for Explicit Hydrodynamics Codes

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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