high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Asynchronous Task-Based Polar Decomposition on Single Node Manycore Architectures

Dalal Sukkari, Hatem Ltaief, Mathieu Faverge, David Keyes

View

Tags: Algorithms, Benchmarking, Computer science, Factorization, Intel Xeon Phi, nVidia, StarPU, Task scheduling, Tesla K80, Tesla P100

September 21, 2017 by hgpu

IBM Deep Learning Service

Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi, Parijat Dube, Ben Herta, Vatche Ishakian, K. R. Jayaram, Rania Khalaf, Avesh Krishna, Yu Bo Li, Vinod Muthusamy, Ruchir Puri, Yufei Ren, Florian Rosenberg, Seetharami R. Seelam, Yandong Wang, Jian Ming Zhang, Li Zhang

View

Tags: Cloud, Computer science, CUDA, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia

September 21, 2017 by hgpu

Distributed Training Large-Scale Deep Architectures

Shang-Xuan Zou, Chun-Yen Chen, Jui-Lin Wu, Chun-Nan Chou, Chia-Chin Tsao, Kuan-Chieh Tung, Ting-Wei Lin, Cheng-Lung Sung, Edward Y. Chang

View

Tags: Algorithms, Benchmarking, Computer science, CUDA, Data parallelism, Deep learning, nVidia, Tesla K80

September 21, 2017 by hgpu

Out-of-core Implementation for Accelerator Kernels on Heterogeneous Clouds

Hamidreza Khaleghzadeh, Ziming Zhong, Ravi Reddy, Alexey Lastovetsky

View

Tags: BLAS, Cloud, Computer science, CUBLAS, CUDA, FPGA, Heterogeneous systems, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package, Virtualization

September 16, 2017 by hgpu

Monte Carlo methods for massively parallel computers

Martin Weigel

View

Tags: CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX Titan X, Phase transition, Physics, Spin Systems, Tesla C1060, Tesla K20

September 16, 2017 by hgpu

Meta Networks for Neural Style Transfer

Falong Shen, Shuicheng Yan, Gang Zeng

View

Tags: Caffe, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package

September 16, 2017 by hgpu

End-to-end Deep Learning of Optimization Heuristics

Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather

View

Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 7970, Compilers, Computer science, Deep learning, Keras, Machine learning, nVidia GeForce GTX 480, OpenCL, Optimization, Package, TensorFlow, Tesla K20, Theano

September 16, 2017 by hgpu

GPU-Accelerated Parallel Finite-Difference Time-Domain Method for Electromagnetic Waves Propagation in Unmagnetized Plasma Media

Xi-min Wang, Lang-lang Xiong, Song Liu, Zhi-yun Peng, Shuang-ying Zhong

View

Tags: Computational Physics, CUDA, FDTD, Finite-difference time-domain, nVidia, nVidia GeForce GT 650 M, Physics, Plasma physics

September 12, 2017 by hgpu

Sorting with GPUs: A Survey

Dmitri I. Arkhipov, Di Wu, Keqin Li, Amelia C. Regan

View

Tags: Algorithms, Computer science, CUDA, nVidia, Sorting, survey

September 12, 2017 by hgpu

Report: Performance comparison between C2075 and P100 GPU cards using cosmological correlation functions

Miguel Cardenas-Montes, Ivan Mendez-Jimenez, Juan Jose Rodriguez-Vazquez, Jose Maria Hernandez Calama

View

Tags: Computer science, CUDA, nVidia, Performance, Tesla C2075, Tesla P100

September 12, 2017 by hgpu

A Comparative Study of 2D Numerical Methods with GPU Computing

Ben J. Zimmerman, Jonathan D. Regele, Bong Wie

View

Tags: Computer science, CUDA, Finite volume method, Numerical simulation, nVidia, Tesla K20

September 12, 2017 by hgpu

Progressive Clustering of Big Data with GPU Acceleration and Visualization

Jun Wang, Eric Papenhausen, Bing Wang, Sungsoo Ha, Alla Zelenyuk, Klaus Mueller

View

Tags: Algorithms, big data, Clustering, Computer science, CUDA, nVidia, Tesla K20, Visualization

September 9, 2017 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

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

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

* * *

* * *

HGPU group © 2010-2025 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: