Tensor Libraries

cuTensor: an Optimized Tensor Library on GPUs

* [TPDS] T. Zhang, X.-Y. Liu (co-primary author), X. Wang, A. Walid. cuTensor-tubal: Efficient primitives for tubal-rank tensor learning operations on GPUs. IEEE Transactions on Parallel and Distributed Systems, 2019.

* [HPCC] H. Li, T. Zhang, R. Zhang, X.-Y. Liu. High-performance tensor decoder on GPUs for wireless camera networks in IoT. IEEE HPCC 2019.

* [HPCC] H. Lu, T. Zhang, X.-Y. Liu. High-performance homomorphic matrix completion on GPUs. IEEE HPCC 2019.

* [ICASSPX.-Y. Liu, T. Zhang (co-primary author). cuTensor-tubal: Optimized GPU library for low-tubal-rank tensors. IEEE ICASSP, 2019. 

* [xxx] X.-Y. Liu, T. Zhang (co-primary author), X. Wang, and A. Walid. cuTensor-tubal: Efficient Primitives for Tubal-rank Tensor Operations on GPUs. (Submitted to) IEEE Transactions on Parallel and Distributed Systems, 2019. 

* 1st GPU system made number ONE on the TOP500  for supercomputers, 2012 fall.  Ever since then, GPUs serve the major computational power source, and driver the AI booming.

       By Oak Ridge National Labs (Oct. 2012)

  • 18, 688 Opteron 16-core CPUs
  • 18, 688 NIVIDIA Tesla K20 GPUs
  • 17.6 peta FLOPS 

* T. Zhang, X.-Y. Liu. cuTensor-tubal: An optimized GPU library for low-tubal-rank tensors. 2018.

* T. Zhang, X.-Y. Liu. cuTensor: Efficient primitives for tensor computations on GPUs. 2018.

Comparing Tesla P100, V100 with CPU (Matlab R2014b)

For tensor decompositions, our cuTensor library achieves the following speedups

 * 15x for tensor size 2000 x 2000 x 128;

* 11.8x for tensor size 2000 x 2000 x 64;

* 10.5x for tensor size 2000 x 2000 x 32;

Tensor Operations

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Tensor Decompositions

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Tensor Algorithms

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