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5 papers · 2 filters
KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training
Truong Thao Nguyen, Balazs Gerofi, Edgar Josafat Martinez-Noriega +2
This paper proposes a method for hiding the least-important samples during the training of deep neural networks to increase efficiency, i.e., to reduce the cost of training. Using…
Exploiting Scratchpad Memory for Deep Temporal Blocking: A case study for 2D Jacobian 5-point iterative stencil kernel (j2d5pt)
Lingqi Zhang, Mohamed Wahib, Peng Chen +4
General Purpose Graphics Processing Units (GPGPU) are used in most of the top systems in HPC. The total capacity of scratchpad memory has increased by more than 40 times in the las…
Revisiting Temporal Blocking Stencil Optimizations
Lingqi Zhang, Mohamed Wahib, Peng Chen +4
Iterative stencils are used widely across the spectrum of High Performance Computing (HPC) applications. Many efforts have been put into optimizing stencil GPU kernels, given the p…
Wilson matrix kernel for lattice QCD on A64FX architecture
Issaku Kanamori, Keigo Nitadori, Hideo Matsufuru
We study the implementation of the even-odd Wilson fermion matrix for lattice QCD simulations on the A64FX architecture. Efficient coding of the stencil operation is investigated f…
Analyzing I/O Performance of a Hierarchical HPC Storage System for Distributed Deep Learning
Takaaki Fukai, Kento Sato, Takahiro Hirofuchi
Today, deep learning is an essential technology for our life. To solve more complex problems with deep learning, both sizes of training datasets and neural networks are increasing.…