NVIDIA cuDSS is a first-generation sparse direct solver library designed to accelerate engineering and scientific computing. cuDSS is increasingly adopted in data centers and other environments and supports single-GPU, multi-GPU and multi-node (MGMN) configurations. cuDSS has become a key tool for accelerating computer-aided engineering (CAE) workflows and scientific computations across��
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As a data scientist, evaluating machine learning model performance is a crucial aspect of your work. To do so effectively, you have a wide range of statistical metrics at your disposal, each with its own unique strengths and weaknesses. By developing a solid understanding of these metrics, you are not only better equipped to choose the best one for optimizing your model but also to explain your��
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Update May 21, 2018: CUTLASS 1.0 is now available as Open Source software at the CUTLASS repository. CUTLASS 1.0 has changed substantially from our preview release described in the blog post below. We have decomposed the structure of the GEMM computation into deeper, structured primitives for loading data, computing predicate masks, streaming data at each level of the GEMM hierarchy��
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A defining feature of the new NVIDIA Volta GPU architecture is Tensor Cores, which give the NVIDIA V100 accelerator a peak throughput that is 12x the 32-bit floating point throughput of the previous-generation NVIDIA P100. Tensor Cores enable you to use mixed-precision for higher throughput without sacrificing accuracy. Tensor Cores provide a huge boost to convolutions and matrix operations.
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There��s a new computational workhorse in town. For decades, general matrix-matrix multiply��known as GEMM in Basic Linear Algebra Subroutines (BLAS) libraries��has been a standard benchmark for computational performance. GEMM is possibly the most optimized and widely used routine in scientific computing. Expert implementations are available for every architecture and quickly achieve the peak��
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[Note: Lung Sheng Chien from NVIDIA also contributed to this post.] A key bottleneck for most science and engineering simulations is the solution of sparse linear systems of equations, which can account for up to 95% of total simulation time. There are two types of solvers for these systems: iterative and direct solvers. Iterative solvers are favored for the largest systems these days (see my��
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[This post was co-written by Everett Phillips and Massimiliano Fatica.] The High Performance Conjugate Gradient Benchmark (HPCG) is a new benchmark intended to complement the High-Performance Linpack (HPL) benchmark currently used to rank supercomputers in the TOP500 list. This new benchmark solves a large sparse linear system using a multigrid preconditioned conjugate gradient (PCG) algorithm.
]]>A Givens rotation [1] represents a rotation in a plane represented by a matrix of the form , where the intersections of the th and th columns contain the values and . Multiplying a vector by a Givens rotation matrix represents a rotation of the vector in the plane by radians. According to Wikipedia, the main use of Givens rotations in numerical linear algebra is to introduce zeros in��
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