Optimization techniques for GPU programming
In the past decade, Graphics Processing Units have played an important role in the field of
high-performance computing and they still advance new fields such as IoT, autonomous …
high-performance computing and they still advance new fields such as IoT, autonomous …
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 …
Accelerating sparse matrix–matrix multiplication with GPU Tensor Cores
Sparse general matrix–matrix multiplication (spGEMM) is an essential component in many
scientific and data analytics applications. However, the sparsity pattern of the input matrices …
scientific and data analytics applications. However, the sparsity pattern of the input matrices …
TileSpGEMM: A tiled algorithm for parallel sparse general matrix-matrix multiplication on GPUs
Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental
building blocks in sparse linear solvers, graph processing frameworks and machine learning …
building blocks in sparse linear solvers, graph processing frameworks and machine learning …
Performance-aware model for sparse matrix-matrix multiplication on the sunway taihulight supercomputer
General sparse matrix-sparse matrix multiplication (SpGEMM) is one of the fundamental
linear operations in a wide variety of scientific applications. To implement efficient SpGEMM …
linear operations in a wide variety of scientific applications. To implement efficient SpGEMM …
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 …
High-performance and memory-saving sparse general matrix-matrix multiplication for nvidia pascal gpu
Y Nagasaka, A Nukada… - 2017 46th International …, 2017 - ieeexplore.ieee.org
Sparse general matrix-matrix multiplication (SpGEMM) is one of the key kernels of
preconditioners such as algebraic multigrid method or graph algorithms. However, the …
preconditioners such as algebraic multigrid method or graph algorithms. However, the …
Porting hypre to heterogeneous computer architectures: Strategies and experiences
Linear systems are occurring in many applications, and solving them can take a large
amount of the total simulation time. The high performance library hypre provides a variety of …
amount of the total simulation time. The high performance library hypre provides a variety of …
High-performance sparse matrix-matrix products on Intel KNL and multicore architectures
Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely
used in areas ranging from traditional numerical applications to recent big data analysis and …
used in areas ranging from traditional numerical applications to recent big data analysis and …
Performance optimization, modeling and analysis of sparse matrix-matrix products on multi-core and many-core processors
Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely
used in areas ranging from traditional numerical applications to recent big data analysis and …
used in areas ranging from traditional numerical applications to recent big data analysis and …