Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Fredrik Kjolstad"'
Autor:
Qiaoyi Liu, Jeff Setter, Dillon Huff, Maxwell Strange, Kathleen Feng, Mark Horowitz, Priyanka Raina, Fredrik Kjolstad
Publikováno v:
ACM Transactions on Architecture and Code Optimization. 20:1-26
Image processing and machine learning applications benefit tremendously from hardware acceleration. Existing compilers target either FPGAs, which sacrifice power and performance for programmability, or ASICs, which become obsolete as applications cha
Autor:
Kalhan Koul, Jackson Melchert, Kavya Sreedhar, Leonard Truong, Gedeon Nyengele, Keyi Zhang, Qiaoyi Liu, Jeff Setter, Po-Han Chen, Yuchen Mei, Maxwell Strange, Ross Daly, Caleb Donovick, Alex Carsello, Taeyoung Kong, Kathleen Feng, Dillon Huff, Ankita Nayak, Rajsekhar Setaluri, James Thomas, Nikhil Bhagdikar, David Durst, Zachary Myers, Nestan Tsiskaridze, Stephen Richardson, Rick Bahr, Kayvon Fatahalian, Pat Hanrahan, Clark Barrett, Mark Horowitz, Christopher Torng, Fredrik Kjolstad, Priyanka Raina
Publikováno v:
ACM Transactions on Embedded Computing Systems. 22:1-34
With the slowing of Moore’s law, computer architects have turned to domain-specific hardware specialization to continue improving the performance and efficiency of computing systems. However, specialization typically entails significant modificatio
Autor:
Kunle Olukotun, Rawn Henry, Stephen Chou, Olivia Hsu, Saman Amarasinghe, Fredrik Kjolstad, Rohan Yadav
Publikováno v:
Proceedings of the ACM on Programming Languages. 5:1-29
This paper shows how to compile sparse array programming languages. A sparse array programming language is an array programming language that supports element-wise application, reduction, and broadcasting of arbitrary functions over dense and sparse
Publikováno v:
Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation.
We introduce DISTAL, a compiler for dense tensor algebra that targets modern distributed and heterogeneous systems. DISTAL lets users independently describe how tensors and computation map onto target machines through separate format and scheduling l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2bf5256d227774b3a971329e07e2e4cf
http://arxiv.org/abs/2203.08069
http://arxiv.org/abs/2203.08069
Autor:
Aart Bik, Penporn Koanantakool, Tatiana Shpeisman, Nicolas Vasilache, Bixia Zheng, Fredrik Kjolstad
Sparse tensors arise in problems in science, engineering, machine learning, and data analytics. Programs that operate on such tensors can exploit sparsity to reduce storage requirements and computational time. Developing and maintaining sparse softwa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::240af2686d1b9c1c39037f1695d1d54e
http://arxiv.org/abs/2202.04305
http://arxiv.org/abs/2202.04305
Real world arrays often contain underlying structure, such as sparsity, runs of repeated values, or symmetry. Specializing for structure yields significant speedups. But automatically generating efficient code for structured data is challenging, espe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3af1fc04cdfb36676ecb723126d6378
We introduce SpDISTAL, a compiler for sparse tensor algebra that targets distributed systems. SpDISTAL combines separate descriptions of tensor algebra expressions, sparse data structures, data distribution, and computation distribution. Thus, it ena
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59bf7410b6b5f9da523681af6d812bb8
Publikováno v:
arXiv
PLDI
PLDI
This paper shows how to generate code that efficiently converts sparse tensors between disparate storage formats (data layouts) such as CSR, DIA, ELL, and many others. We decompose sparse tensor conversion into three logical phases: coordinate remapp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::194ecebc3e676cd2b32f0cd903e0c505
https://hdl.handle.net/1721.1/132254
https://hdl.handle.net/1721.1/132254