high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

The Dual-Path Execution Model for Efficient GPU Control Flow

Minsoo Rhu, Mattan Erez

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

February 14, 2013 by hgpu

Document Stream Clustering using GPUs

Michael J. Szaszy, Hanan Samet

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Tags: Algorithms, Clustering, Computer science, CUDA, Information Retrieval, nVidia, nVidia GeForce GTX 480, Performance

February 9, 2013 by hgpu

Effectiveness of program transformations and compilers for directive-based GPU programming models

Swapnil Avinash Ghike

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, Optimization, Performance, Tesla X2090, Thesis

February 9, 2013 by hgpu

Using Hybrid Shared and Distributed Caching for Mixed-Coherency GPU Workloads

Nasser Anssari

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Tags: Computer science, CUDA, nVidia, Performance, Thesis

February 9, 2013 by hgpu

Performance Evaluation of Sparse Matrix Multiplication Kernels on Intel Xeon Phi

Erik Saule, Kamer Kaya, Umit V. Catalyurek

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Tags: Computer science, CUDA, Hardware Architecture, Intel, Intel Phi, Matrix multiplication, nVidia, Performance, Sparse matrix, Tesla C2050, Tesla K20

February 6, 2013 by hgpu

An Implementation of Conflict-Free Offline Permutation on the GPU

Akihiko Kasagi, Koji Nakano, Yasuaki Ito

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

February 6, 2013 by hgpu

Portable Performance on Heterogeneous Architectures

Phitchaya Mangpo Phothilimthana, Jason Ansel, Jonathan Ragan-Kelley, Saman Amarasinghe

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Tags: Artificial intelligence, ATI, ATI Radeon HD 6630 M, Compilers, Computer science, CUDA, Heterogeneous systems, Memory model, nVidia, OpenCL, Performance, Tesla C2070

February 2, 2013 by hgpu

Improving GPGPU Concurrency with Elastic Kernels

Sreepathi Pai, Matthew J. Thazhuthaveetil, R. Govindarajan

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Tags: Computer science, CUDA, nVidia, Performance, Tesla C2070

February 2, 2013 by hgpu

OWL: Cooperative Thread Array Aware Scheduling Techniques for Improving GPGPU Performance

Adwait Jog, Onur Kayiran, Nachiappan Chidambaram Nachiappan, Asit K. Mishra, Mahmut T. Kandemir, Onur Mutlu, Ravishankar Iyer, Chita R. Das

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Performance, Prefetch, Programming techniques

January 31, 2013 by hgpu

Survey On The Off-Chip Scheduling of Memory Accesses in the Memory Interface Of GPUs

Luis Angel Garrido Platero

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Tags: Computer science, CUDA, GPGPU-sim, Hardware Architecture, Memory, nVidia, Performance

January 31, 2013 by hgpu

Exploring Different Automata Representations for Efficient Regular Expression Matching on GPUs

Xiaodong Yu, Michela Becchi

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Tags: Cellular automata, Computer science, CUDA, nVidia, Performance, Security

January 28, 2013 by hgpu

Warped Register File: A Power Efficient Register File for GPGPUs

Mohammad Abdel-Majeed, Murali Annavaram

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

January 28, 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

The Dual-Path Execution Model for Efficient GPU Control Flow

Document Stream Clustering using GPUs

Effectiveness of program transformations and compilers for directive-based GPU programming models

Using Hybrid Shared and Distributed Caching for Mixed-Coherency GPU Workloads

Performance Evaluation of Sparse Matrix Multiplication Kernels on Intel Xeon Phi

An Implementation of Conflict-Free Offline Permutation on the GPU

Portable Performance on Heterogeneous Architectures

Improving GPGPU Concurrency with Elastic Kernels

OWL: Cooperative Thread Array Aware Scheduling Techniques for Improving GPGPU Performance

Survey On The Off-Chip Scheduling of Memory Accesses in the Memory Interface Of GPUs

Exploring Different Automata Representations for Efficient Regular Expression Matching on GPUs

Warped Register File: A Power Efficient Register File for GPGPUs

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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