A survey on techniques for cooperative CPU-GPU computing
K Raju, NN Chiplunkar - Sustainable Computing: Informatics and Systems, 2018 - Elsevier
Abstract Graphical Processing Unit provides massive parallelism due to the presence of
hundreds of cores. Usage of GPUs for general purpose computation (GPGPU) has resulted …
hundreds of cores. Usage of GPUs for general purpose computation (GPGPU) has resulted …
High-performance packet classification on GPU
S Zhou, SG Singapura… - 2014 IEEE High …, 2014 - ieeexplore.ieee.org
Multi-field packet classification is a network kernel function where packets are classified and
routed based on a predefined rule set. Recently, there has been a new trend in exploring …
routed based on a predefined rule set. Recently, there has been a new trend in exploring …
A survey on parallel computing for traditional computer vision
The applications of computer vision (CV) are continuously increasing along with the
enormous demand for real‐time data processing. This visual data processing is done with …
enormous demand for real‐time data processing. This visual data processing is done with …
COX: Exposing CUDA warp-level functions to CPUs
As CUDA becomes the de facto programming language among data parallel applications
such as high-performance computing or machine learning applications, running CUDA on …
such as high-performance computing or machine learning applications, running CUDA on …
Cupbop: Making cuda a portable language
CUDA is designed specifically for NVIDIA GPUs and is not compatible with non-NVIDIA
devices. Enabling CUDA execution on alternative backends could greatly benefit the …
devices. Enabling CUDA execution on alternative backends could greatly benefit the …
An effective clustering algorithm to index high dimensional metric spaces
A metric space consists of a collection of objects and a distance function defined among
them, which satisfies the triangular inequality. The goal is to preprocess the set so that, given …
them, which satisfies the triangular inequality. The goal is to preprocess the set so that, given …
A unified CPU-GPU protocol for GNN training
Training a Graph Neural Network (GNN) model on large-scale graphs involves a high
volume of data communication and computations. While state-of-the-art CPUs and GPUs …
volume of data communication and computations. While state-of-the-art CPUs and GPUs …
Performance improvement of CUDA applications by reducing CPU-GPU data transfer overhead
NV Sunitha, K Raju… - … international conference on …, 2017 - ieeexplore.ieee.org
In a CPU-GPU based heterogeneous computing system, the input data to be processed by
the kernel resides in the host memory. The host and the device memory address spaces are …
the kernel resides in the host memory. The host and the device memory address spaces are …
Parallel CPU–GPU computing technique for discrete element method
V Skorych, M Dosta - Concurrency and Computation: Practice …, 2022 - Wiley Online Library
The efficiency of the simulations with the discrete element method (DEM) is significantly
improved using a novel computational strategy. The new method is developed with a focus …
improved using a novel computational strategy. The new method is developed with a focus …
A high performance implementation of spectral clustering on cpu-gpu platforms
Y Jin, JF Jaja - 2016 IEEE International Parallel and Distributed …, 2016 - ieeexplore.ieee.org
Spectral clustering is one of the most popular graph clustering algorithms, which achieves
the best performance for many scientific and engineering applications. However, existing …
the best performance for many scientific and engineering applications. However, existing …