high performance computing on graphics processing units: hgpu.org

hgpu.org » Computer science

CudaChain: A Practical GPU-accelerated 2D Convex Hull Algorithm

Gang Mei

View

Download (PDF)

Tags: Computational Geometry, Computer science, CUDA, nVidia, nVidia GeForce GT 640, nVidia GeForce GTX 660 M, Sorting

August 27, 2015 by hgpu

gScan: Accelerating Graham Scan on the GPU

Gang Mei

View

Download (PDF)

Tags: Algorithms, Computational Geometry, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GT 640, nVidia GeForce GTX 660 M, Sorting

August 27, 2015 by hgpu

Accelerated Deep Learning using Intel Xeon Phi

Andre Viebke

View

Download (PDF)

Tags: Computer science, Computer vision, Deep learning, Intel Xeon Phi, Machine learning, Neural networks, OpenMP, Thesis

August 27, 2015 by hgpu

MemcachedGPU: Scaling-up Scale-out Key-value Stores

Tayler H. Hetherington, Mike O'Connor, Tor M. Aamodt

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Databases, Energy-efficient computing, nVidia, nVidia GeForce GTX 750 Ti, Package, Tesla K20, Tesla K40

August 27, 2015 by hgpu

Viability of Feature Detection on Sony Xperia Z3 using OpenCL

Max Danielsson, Thomas Sievert

View

Download (PDF)

Source codes

Tags: Android, Computer science, Computer vision, Embedded high-performance computing, nVidia, nVidia GeForce GTX 660, OpenCL, Package, Thesis

August 24, 2015 by hgpu

Performance Evaluations of Document-Oriented Databases using GPU and Cache Structure

Shin Morishima, Hiroki Matsutani

View

Download (PDF)

Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 980, Performance

August 24, 2015 by hgpu

Scheduling for new computing platforms with GPUs

Florence Monna

View

Download (PDF)

Tags: Computer science, Data Structures and Algorithms, nVidia, OpenCL, Task scheduling, Tesla C2050, Thesis

August 24, 2015 by hgpu

Source-to-Source Automatic Program Transformations for GPU-like Hardware Accelerators

Mehdi Amini

View

Download (PDF)

Tags: ATI, ATI Radeon HD 6970, Code generation, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 670, OpenCL, Tesla C1060, Tesla C2070, Thesis

August 24, 2015 by hgpu

Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

Xiangyun Hu, Lizhi Ye, Shiyan Pang, Jie Shan

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX 690, Sensing

August 24, 2015 by hgpu

A CPU and GPU Heterogeneous Processing of Multimedia Data by using OpenCL

Heegon Kim, Sungju Lee, Yongwha Chung, Daihee Park

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 560, OpenCL

August 21, 2015 by hgpu

Locality Aware Work-Stealing Based Scheduling in Hybrid CPU-GPU

A. Tarun Beri, B. Sorav Bansal, C. Subodh Kumar

View

Download (PDF)

Tags: Computer science, CUDA, Distributed computing, nVidia, Task scheduling, Tesla M2070

August 21, 2015 by hgpu

A Parallelizing Matlab Compiler Framework and Run time for Heterogeneous Systems

Sam Skalicky, Sonia Lopez, Marcin Lukowiak, Andrew G. Schmidt

View

Download (PDF)

Tags: Code generation, Computer science, FPGA, Heterogeneous systems, Matlab

August 21, 2015 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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

CudaChain: A Practical GPU-accelerated 2D Convex Hull Algorithm

gScan: Accelerating Graham Scan on the GPU

Accelerated Deep Learning using Intel Xeon Phi

MemcachedGPU: Scaling-up Scale-out Key-value Stores

Viability of Feature Detection on Sony Xperia Z3 using OpenCL

Performance Evaluations of Document-Oriented Databases using GPU and Cache Structure

Scheduling for new computing platforms with GPUs

Source-to-Source Automatic Program Transformations for GPU-like Hardware Accelerators

Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

A CPU and GPU Heterogeneous Processing of Multimedia Data by using OpenCL

Locality Aware Work-Stealing Based Scheduling in Hybrid CPU-GPU

A Parallelizing Matlab Compiler Framework and Run time for Heterogeneous Systems

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

Most viewed papers (last 30 days)