high performance computing on graphics processing units: hgpu.org

hgpu.org » Memory model

VirtCL: a framework for OpenCL device abstraction and management

Yi-Ping You, Hen-Jung Wu, Yeh-Ning Tsai, Yen-Ting Chao

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Tags: Computer science, Heterogeneous systems, Memory model, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 590, OpenCL, Performance

February 23, 2016 by hgpu

Exploiting Hyper-Loop Parallelism in Vectorization to Improve Memory Performance on CUDA GPGPU

Shixiong Xu, David Gregg

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Tags: ARM, Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 645, nVidia Jetson TK1

September 3, 2015 by hgpu

Dynamic Memory Allocation for OpenCL

Nadir Gamal Abdelrahim Salih

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Tags: ATI, ATI Radeon HD 7400 M, Computer science, Memory model, OpenCL, Thesis

August 31, 2015 by hgpu

Overhauling SC atomics in C11 and OpenCL

John Wickerson

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Tags: Computer science, Heterogeneous systems, Memory model, OpenCL

July 17, 2015 by hgpu

Automatic Optimization of Thread Mapping for a GPGPU Programming Framework

Kazuhiko Ohno, Tomoharu Kamiya, Takanori Maruyama, Masaki Matsumoto

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Tags: Benchmarking, Compilers, Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 980

July 15, 2015 by hgpu

Taming the complexities of the C11 and OpenCL memory models

John Wickerson, Mark Batty

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Tags: Computer science, Heterogeneous systems, Memory model, OpenCL, Package

January 23, 2015 by hgpu

A Time Optimal Parallel Algorithm for the Dynamic Programming on the Hierarchical Memory Machine

Koji Nakano

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

January 13, 2015 by hgpu

A Survey Of Architectural Approaches for Managing Embedded DRAM and Non-volatile On-chip Caches

Sparsh Mittal; Jeffrey S. Vetter; Dong Li

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Tags: cache, Compiler, cpu, Energy-efficient computing, GPU, Hardware Architecture, Memory model

January 12, 2015 by sparsh0mittal

Testing and Exposing Weak Graphics Processing Unit Memory Models

Tyler Rey Sorensen

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, PTX, Tesla C2075, Thesis

October 27, 2014 by hgpu

Random Address Permute-Shift Technique for the Shared Memory on GPUs

Koji Nakano, Susumu Matsumae, Yasuaki Ito

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX Titan

October 14, 2014 by hgpu

Supporting Applications Involving Dynamic Data Structures and Irregular Memory Access on Emerging Parallel Platforms

Bin Ren

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Tags: Algorithms, Computer science, CUDA, Intel Xeon Phi, Memory model, nVidia, nVidia Quadro FX 5800, Prefetch, Tesla C2050, Thesis

September 19, 2014 by hgpu

Professional CUDA C Programming

John Cheng, Max Grossman, Ty McKercher

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Tags: Algorithms, Book, Computer science, CUDA, Memory model, nVidia, Review

September 8, 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

VirtCL: a framework for OpenCL device abstraction and management

Exploiting Hyper-Loop Parallelism in Vectorization to Improve Memory Performance on CUDA GPGPU

Dynamic Memory Allocation for OpenCL

Overhauling SC atomics in C11 and OpenCL

Automatic Optimization of Thread Mapping for a GPGPU Programming Framework

Taming the complexities of the C11 and OpenCL memory models

A Time Optimal Parallel Algorithm for the Dynamic Programming on the Hierarchical Memory Machine

A Survey Of Architectural Approaches for Managing Embedded DRAM and Non-volatile On-chip Caches

Testing and Exposing Weak Graphics Processing Unit Memory Models

Random Address Permute-Shift Technique for the Shared Memory on GPUs

Supporting Applications Involving Dynamic Data Structures and Irregular Memory Access on Emerging Parallel Platforms

Professional CUDA C Programming

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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