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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

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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

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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

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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

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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

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

September 16, 2017 by hgpu

Sorting with GPUs: A Survey

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

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

September 12, 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

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

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

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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

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

September 12, 2017 by hgpu

Beyond 16GB: Out-of-Core Stencil Computations

Istvan Z. Reguly, Gihan R. Mudalige, Michael B. Giles

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Tags: Computer science, CUDA, Intel Xeon Phi, MPI, nVidia, Stencil computation, Tesla P100

September 9, 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

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

September 9, 2017 by hgpu

Data Layout Oriented Compilation Techniques in Vectorization for Multi-/Many-cores

Shixiong Xu

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 645, nVidia Jetson TK1

September 9, 2017 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

HiRep

Scaling SU(2) to 1000 GPUs using HiRep

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

Gaia AVU-GSR

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

Over-synchronization in GPU Programs

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