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TTLG - An Efficient Tensor Transposition Library for GPUs
- Authors
- Vedurada, J.; Suresh, A.; Rajam, A.S.; Kim, J.; Hong, C.; Panyala, A.; Krishnamoorthy, S.; Nandivada, V.K.; Srivastava, R.K.; Sadayappan, P.
- Issue Date
- May-2018
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- GPU; High performance; Tensor Transpose
- Citation
- Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018, pp 578 - 588
- Pages
- 11
- Journal Title
- Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018
- Start Page
- 578
- End Page
- 588
- Abstract
- This paper presents a Tensor Transposition Library for GPUs (TTLG). A distinguishing feature of TTLG is that it also includes a performance prediction model, which can be used by higher level optimizers that use tensor transposition. For example, tensor contractions are often implemented by using the TTGT (Transpose-Transpose-GEMM-Transpose) approach-transpose input tensors to a suitable layout and then use high-performance matrix multiplication followed by transposition of the result. The performance model is also used internally by TTLG for choosing among alternative kernels and/or slicing/blocking parameters for the transposition. TTLG is compared with current state-of-The-Art alternatives for GPUs. Comparable or better transposition times for the 'repeated-use' scenario and considerably better 'single-use' performance are observed. © 2018 IEEE.
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