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DTSTART;TZID=America/Chicago:20211114T153000
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UID:submissions.supercomputing.org_SC21_sess431_ws_llvmf103@linklings.com
SUMMARY:A High Performance Sparse Tensor Algebra Compiler in MLIR
DESCRIPTION:Workshop\n\nA High Performance Sparse Tensor Algebra Compiler 
 in MLIR\n\nTian, Guo, Li, Ren, Kestor\n\nSparse tensor algebra is widely u
 sed in many applications.  The performance of sparse tensor algebra kernel
 s strongly depends on the characteristics of the input tensors, hence many
  storage formats are designed for tensors to achieve optimal performance f
 or particular applications/architectures, which makes it challenging to im
 plement and optimize every tensor operation of interest on a given archite
 cture.  We propose a tensor algebra domain-specific language (DSL) and com
 piler framework to automatically generate kernels for mixed sparse-dense t
 ensor algebra operations.\n\nThe proposed DSL provides high-level programm
 ing abstractions to represent tensor algebra operations.  The compiler int
 roduces a new Sparse Tensor Algebra dialect built on top of LLVM's extensi
 ble MLIR compiler infrastructure for efficient code generation while cover
 ing a wide range of tensor formats.  It also leverages input-dependent cod
 e optimization to enhance data locality for better performance.  Our resul
 ts show that the performance of automatically generated kernels outperform
 s the state-of-the-art sparse tensor algebra compiler.\n\nTag: Parallel Pr
 ogramming Systems\n\nRegistration Category: Workshop Reg Pass
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