- Home
- Search Results
- Page 1 of 1
Search for: All records
-
Total Resources2
- Resource Type
-
20000
- Availability
-
20
- Author / Contributor
- Filter by Author / Creator
-
-
Bhetwal, Kalyan (2)
-
Hall, Mary (2)
-
Olschanowsky, Catherine (2)
-
Popoola, Tobi (2)
-
St. George, Aaron (2)
-
Zhao, Tuowen (2)
-
Strout, Michelle (1)
-
Strout, Michelle Mills (1)
-
#Tyler Phillips, Kenneth E. (0)
-
#Willis, Ciara (0)
-
& Abreu-Ramos, E. D. (0)
-
& Abramson, C. I. (0)
-
& Abreu-Ramos, E. D. (0)
-
& Adams, S.G. (0)
-
& Ahmed, K. (0)
-
& Ahmed, Khadija. (0)
-
& Akcil-Okan, O. (0)
-
& Akuom, D. (0)
-
& Aleven, V. (0)
-
& Andrews-Larson, C. (0)
-
- Filter by Editor
-
-
& Spizer, S. M. (0)
-
& . Spizer, S. (0)
-
& Ahn, J. (0)
-
& Bateiha, S. (0)
-
& Bosch, N. (0)
-
& Brennan K. (0)
-
& Brennan, K. (0)
-
& Chen, B. (0)
-
& Chen, Bodong (0)
-
& Drown, S. (0)
-
& Ferretti, F. (0)
-
& Higgins, A. (0)
-
& J. Peters (0)
-
& Kali, Y. (0)
-
& Ruiz-Arias, P.M. (0)
-
& S. Spitzer (0)
-
& Spitzer, S. (0)
-
& Spitzer, S.M. (0)
-
(submitted - in Review for IEEE ICASSP-2024) (0)
-
- (0)
-
-
Have feedback or suggestions for a way to improve these results?
!
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
Many scientific applications compute using sparse data and store that data in a variety of sparse formats because each format has unique space and performance benefits. Optimizing applications that use sparse data involves translating the sparse data into the chosen format and transforming the computation to iterate over that format. This paper presents a formal definition of sparse tensor formats and an automated approach to synthesize the transformation between formats. This approach is unique in that it supports ordering constraints not supported by other approaches and synthesizes the transformation code in a high-level intermediate representation suitable for applying composable transformations such as loop fusion and temporary storage reduction. We demonstrate that the synthesized code for COO to CSR with optimizations is 2.85x faster than TACO, Intel MKL, and SPARSKIT while the more complex COO to DIA is 1.4x slower than TACO but faster than SPARSKIT and Intel MKL using the geometric average of execution time.more » « less
-
Popoola, Tobi ; Zhao, Tuowen ; St. George, Aaron ; Bhetwal, Kalyan ; Strout, Michelle ; Hall, Mary ; Olschanowsky, Catherine ( , International Symposium on Code Generation and Optimization)Many scientific applications compute on sparse data and use a variety of sparse formats because each format has unique space and performance benefits. Optimizing applications that use sparse data involves translating the sparse data into the chosen format and transforming the computation to iterate over that format. This paper presents a formal definition of sparse tensor formats and an automated approach to synthesize the transformation between formats. This approach is unique in that it supports ordering constraints not supported by other approaches and synthesizes the transformation code in a high-level intermediate representation suitable for applying composable transformations such as loop fusion and temporary storay reduction. We demonstrate that the synthesized code for COO to CSR with optimizations is 3.4X faster than TACO, Intel MKL and SPARSKIT while the more complex COO to DIA is slower than TACO but competitive with Intel MKL and SPARSKIT.more » « less