A systematic survey of general sparse matrix-matrix multiplication
General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from
researchers in graph analyzing, scientific computing, and deep learning. Many optimization …
researchers in graph analyzing, scientific computing, and deep learning. Many optimization …
Dissecting tensor cores via microbenchmarks: Latency, throughput and numeric behaviors
Tensor Cores have been an important unit to accelerate Fused Matrix Multiplication
Accumulation (MMA) in all NVIDIA GPUs since Volta Architecture. To program Tensor Cores …
Accumulation (MMA) in all NVIDIA GPUs since Volta Architecture. To program Tensor Cores …
AccelTran: A sparsity-aware accelerator for dynamic inference with transformers
Self-attention-based transformer models have achieved tremendous success in the domain
of natural language processing. Despite their efficacy, accelerating the transformer is …
of natural language processing. Despite their efficacy, accelerating the transformer is …
TANGO: re-thinking quantization for graph neural network training on GPUs
Graph learning is becoming increasingly popular due to its superior performance in tackling
many grand challenges. While quantization is widely used to accelerate Graph Neural …
many grand challenges. While quantization is widely used to accelerate Graph Neural …
DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication
Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and
engineering, graph processing, and machine learning applications. Much work on SpMV …
engineering, graph processing, and machine learning applications. Much work on SpMV …
Accelerating Graph Neural Networks on Real Processing-In-Memory Systems
C Giannoula, P Yang, I Fernandez Vega… - arXiv e …, 2024 - ui.adsabs.harvard.edu
Abstract Graph Neural Networks (GNNs) are emerging ML models to analyze graph-
structure data. Graph Neural Network (GNN) execution involves both compute-intensive and …
structure data. Graph Neural Network (GNN) execution involves both compute-intensive and …
HASpGEMM: Heterogeneity-Aware Sparse General Matrix-Matrix Multiplication on Modern Asymmetric Multicore Processors
Sparse general matrix-matrix multiplication (SpGEMM) is an important kernel in
computational science and engineering, and has been widely studied on homogeneous …
computational science and engineering, and has been widely studied on homogeneous …
Efficient execution of SpGEMM on long vector architectures
V Le Fèvre, M Casas - … of the 32nd International Symposium on High …, 2023 - dl.acm.org
The Sparse GEneral Matrix-Matrix multiplication (SpGEMM) C= A x B is a fundamental
routine extensively used in domains like machine learning or graph analytics. Despite its …
routine extensively used in domains like machine learning or graph analytics. Despite its …
On Efficient Large Sparse Matrix Chain Multiplication
Sparse matrices are often used to model the interactions among different objects and they
are prevalent in many areas including e-commerce, social network, and biology. As one of …
are prevalent in many areas including e-commerce, social network, and biology. As one of …
HARP: Hardware-Based Pseudo-Tiling for Sparse Matrix Multiplication Accelerator
General sparse matrix-matrix multiplication (SpGEMM) is a memory-bound workload, due to
the compression format used. To minimize data movements for input matrices, outer product …
the compression format used. To minimize data movements for input matrices, outer product …