Sparse tensors are vectors, matrices, and higher-dimensional generalizations with many zeros. They are crucial in various fields such as scientific computing,...

Sparse tensors are vectors, matrices, and higher-dimensional generalizations with many zeros. They are crucial in various fields such as scientific computing, signal processing, and deep learning due to their efficiency in storage, computation, and power. Despite their benefits, handling sparse tensors manually or through existing libraries is often cumbersome, error-prone, nonportable…

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This blog post is part of a series designed to help developers learn NVIDIA CUDA Tile programming for building high-performance GPU kernels, using matrix...

This blog post is part of a series designed to help developers learn NVIDIA CUDA Tile programming for building high-performance GPU kernels, using matrix multiplication as a core example. In this post, you’ll learn: Before you begin, be sure your environment meets the following requirements (see the quickstart for more information): Environment requirements: Install…

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Simulating large-scale quantum computers has become more difficult as the quality of quantum processing units (QPUs) improves. Validating the results is key to...

Simulating large-scale quantum computers has become more difficult as the quality of quantum processing units (QPUs) improves. Validating the results is key to ensure that after the devices scale beyond what is classically simulable, we can still trust the outputs. Similarly, when generating large-scale datasets for various AI models that aim to aid in the operation of quantum processors…

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The release of NVIDIA CUDA 13.1 introduces tile-based programming for GPUs, making it one of the most fundamental additions to GPU programming since CUDA was...

The release of NVIDIA CUDA 13.1 introduces tile-based programming for GPUs, making it one of the most fundamental additions to GPU programming since CUDA was invented. Writing GPU tile kernels enables you to write your algorithm at a higher level than a single-instruction multiple-thread (SIMT) model, while the compiler and runtime handle the partitioning of work onto threads under the covers.

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With its largest advancement since the NVIDIA CUDA platform was invented in 2006, CUDA 13.1 is launching NVIDIA CUDA Tile. This exciting innovation introduces a...

With its largest advancement since the NVIDIA CUDA platform was invented in 2006, CUDA 13.1 is launching NVIDIA CUDA Tile. This exciting innovation introduces a virtual instruction set for tile-based parallel programming, focusing on the ability to write algorithms at a higher level and abstract away the details of specialized hardware, such as tensor cores. CUDA exposes a single…

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CuTe, a core component of CUTLASS 3.x, provides a unified algebra for describing data layouts and thread mappings, and abstracts complex memory access patterns...

CuTe, a core component of CUTLASS 3.x, provides a unified algebra for describing data layouts and thread mappings, and abstracts complex memory access patterns into composable mathematical operations. While CUTLASS 3.x and CuTe have empowered kernel developers to achieve peak performance on Tensor Cores through intuitive abstractions, the extensive use of C++ templates has resulted in high…

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