high performance computing on graphics processing units: hgpu.org

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Optimization solutions for the segmented sum algorithmic function

Alexandru Pirjan

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Tags: Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Performance

September 21, 2013 by hgpu

Optimal Configuration of GPU Cache Memory to Maximize the Performance

Leonid Djinevski, Sime Arsenovski, Sasko Ristov, Marjan Gusev

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, Memory, nVidia, nVidia GeForce GTX 680, Performance

September 16, 2013 by hgpu

On the Performance and Energy-efficiency of Multi-core SIMD CPUs and CUDA-enabled GPUs

Ronald Duarte, Resit Sendag, Frederick J. Vetter

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 580, Performance

September 16, 2013 by hgpu

Algorithmic GPGPU Memory Optimization

Byunghyun Jang, Minsu Choi, Kyung Ki Kim

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Tags: Algorithms, Computer science, Heterogeneous systems, Memory model, nVidia, nVidia GeForce GTX 285, OpenCL, Performance

September 15, 2013 by hgpu

Porting to the Intel Xeon Phi: Opportunities and Challenges

C. Rosales

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Intel Phi, MPI, nVidia, Optimization, Performance

September 15, 2013 by hgpu

Increasing GPU Throughput using Kernel Interleaved Thread Block Scheduling

Mihir Awatramani, Joseph Zambreno, Diane Rover

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

September 13, 2013 by hgpu

Fast computation of computer-generated hologram using Xeon Phi coprocessor

Koki Murano, Tomoyoshi Shimobaba, Atsushi Sugiyama, Naoki Takada, Takashi Kakue, Minoru Oikawa, Tomoyoshi Ito

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Tags: Algorithms, Computational Physics, Computer holography, CUDA, Intel Phi, nVidia, Performance, Physics, Tesla K20

September 13, 2013 by hgpu

Memory transfer optimization for a lattice Boltzmann solver on Kepler architecture nVidia GPUs

Mark Mawson, Alistair Revell

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Tags: CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia Quadro K5000, Performance, Review, Tesla K20

September 11, 2013 by hgpu

A Bi-objective Optimization Framework for Query Plans

P. Przymus, K. Kaczmarski, K. Stencel

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Tags: Heterogeneous systems, Performance, Programming techniques

September 7, 2013 by hgpu

D5.5.3 – Design and implementation of the SIMD-MIMD GPU architecture

J. Lucas, S. Lal, M. Alvarez-Mesa, A. Alhossini, B. Juurlink

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Tags: CUDA, Performance, Power-efficient computing

September 7, 2013 by hgpu

A Survey on GPU System Considering its Performance on Different Applications

D. Londhe, P. Barapatre, N. Gholap,S. Das

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Tags: CUDA, nVidia, Performance, Review

September 6, 2013 by hgpu

Skew Handling in Aggregate Streaming Queries on GPUs

G. Koutsoumpakis, I. Koutsoumpakis, A. Gounaris

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Tags: CUDA, Databases, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 550 Ti, Performance

September 6, 2013 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

Optimization solutions for the segmented sum algorithmic function

Optimal Configuration of GPU Cache Memory to Maximize the Performance

On the Performance and Energy-efficiency of Multi-core SIMD CPUs and CUDA-enabled GPUs

Algorithmic GPGPU Memory Optimization

Porting to the Intel Xeon Phi: Opportunities and Challenges

Increasing GPU Throughput using Kernel Interleaved Thread Block Scheduling

Fast computation of computer-generated hologram using Xeon Phi coprocessor

Memory transfer optimization for a lattice Boltzmann solver on Kepler architecture nVidia GPUs

A Bi-objective Optimization Framework for Query Plans

D5.5.3 – Design and implementation of the SIMD-MIMD GPU architecture

A Survey on GPU System Considering its Performance on Different Applications

Skew Handling in Aggregate Streaming Queries on GPUs

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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