37 citations · 38 across the 9 of their papers we have counts for
5 papers · 1 filter
A Novel Compiler Transformation for Fast Sparse Matrix Multiplication in GPUs
Hossein Albakri, Kazem Cheshmi
Sparse data structures are commonly used in neural networks to reduce the memory footprint. These data structures are compact but cause irregularities such as random memory accesse…
Vectorizing Sparse Matrix Codes with Dependency Driven Trace Analysis
Zachary Cetinic, Kazem Cheshmi, Maryam Mehri Dehnavi
Sparse computations frequently appear in scientific simulations and the performance of these simulations rely heavily on the optimization of the sparse codes. The compact data stru…
Composing Loop-carried Dependence with Other Loops
Kazem Cheshmi, Michelle Mills Strout, Maryam Mehri Dehnavi
Sparse fusion is a compile-time loop transformation and runtime scheduling implemented as a domain-specific code generator. Sparse fusion generates efficient parallel code for the…
Sparse Matrix Code Dependence Analysis Simplification at Compile Time
Mahdi Soltan Mohammadi, Kazem Cheshmi, Ganesh Gopalakrishnan +5
Analyzing array-based computations to determine data dependences is useful for many applications including automatic parallelization, race detection, computation and communication…
Sympiler: Transforming Sparse Matrix Codes by Decoupling Symbolic Analysis
Kazem Cheshmi, Shoaib Kamil, Michelle Mills Strout +1
Sympiler is a domain-specific code generator that optimizes sparse matrix computations by decoupling the symbolic analysis phase from the numerical manipulation stage in sparse cod…