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Showing 1 to 10 of 10 for “"Sparse Tensor Algebra"”.
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Sparse tensor algebra compilation
This dissertation shows how to compile any sparse tensor algebra expression to CPU and GPU code that matches the performance of hand-optimized implementations. A tensor algebra expression is sparse if at least one of its tensor operands is sparse, and a tensor is sparse if most of its values are …
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GSTACO: A Generalized Sparse Tensor Algebra Compiler
… and computer science are characterized by sparse multi-dimensional data. Therefore, optimizations for sparse tensor algebra have received a lot of attention lately. Several hardware and software solutions have emerged in order to speed up the computation of certain tensor expressions, but …
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Format Abstractions for the Compilation of Sparse Tensor Algebra
Tensors are commonly used to represent data in many domains, including data analytics, machine learning, science, and engineering. Many highly-optimized libraries and compilers have been developed for efficiently computing on dense tensors. However, existing libraries and compilers are limited in …
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Tailors: Accelerating Sparse Tensor Algebra by Overbooking Buffer Capacity
Sparse tensor algebra is a challenging class of workloads to accelerate due to few opportunities for data reuse and varying sparsity patterns. Prior sparse tensor algebra accelerators have explored tiling sparse tensors to increase exploitable data reuse and improve throughput, but typically …
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A unified iteration space transformation framework for sparse and dense tensor algebra
… work addresses the problem of optimizing mixed sparse and dense tensor algebra in a compiler. I show that standard loop transformations, such as strip-mining, tiling, collapsing, parallelization and vectorization, can be applied to irregular loops over sparse iteration spaces. I also show how …
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Unified Compilation for Lossless Compression and Sparse Computing
Achieving high performance for computations on tensors depends heavily on the formats used to store them. While sparse tensors are very common, there are more general patterns in data which can sometimes be better captured using lossless compression. We show how to extend sparse tensor algebra …
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SuperTaco : Taco Tensor Algebra kernels on distributed systems using Legion
Tensor algebra is a powerful language for expressing computation on multidimensional data. While many tensor datasets are sparse, most tensor algebra libraries have limited support for handling sparsity. The Tensor Algebra Compiler (Taco) has introduced a taxonomy for sparse tensor formats that has …
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Automated Implementation of Advanced Electronic Structure Methods
… bottleneck. As the rank of the associated tensors increases, the governing equations explode in complexity, rendering manual implementation labor-intensive, error-prone, and difficult to optimize for modern hardware. To address this challenge, this dissertation presents SeQuant, a …
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Toward Practical Quantum Computing Systems with Intelligent Cross-Stack Co-Design
… decoding, and SpArch accelerator, designed for sparse tensor algebra for efficient quantum control signals generations.
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Efficient memory access in modern accelerators
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01