News
On evaluating their most recent GPU code to solve a sparse triangular matrix, Li observes, “Our triangular solver is on average 5.5× faster than NVIDIA’s cuSPARSE. For a matrix from the ExaSGD ECP ...
Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large ...
A new whitepaper looks at Accelerated Sparse Triangular Linear Systems: “Fresh from NVIDIA Numeric Libraries Team, a white paper illustrating the use the CUSPARSE and CUBLAS libraries to achieve a 2x ...
A combined sparse matrix and incomplete factorial screens, samples pH 3.5 to 8.5, low ionic strength, high ionic strength and mixed polymer/salt conditions and halides for potential phasing (see ...
A novel AI-acceleration paper presents a method to optimize sparse matrix multiplication for machine learning models, particularly focusing on structured sparsity. Structured sparsity involves a ...
Real PIM systems can provide high levels of parallelism, large aggregate memory bandwidth and low memory access latency, thereby being a good fit to accelerate the widely-used, memory-bound Sparse ...
Triangular forms may be obtained constructively using sequences of elementary transformations based on elementary matrix algebra. Our main result is to define a unique canonical form for prime-power ...
Results that may be inaccessible to you are currently showing.
Hide inaccessible results