LOS ALAMITOS, Calif., August 29, 2017 – The Association for Computing Machinery (ACM) and the IEEE Computer Society (IEEE-CS) jointly announced today that Shaden Smith of the University of Minnesota and Yang You of the University of California, Berkeley are the recipients of the 2017 ACM/IEEE-CS George Michael Memorial HPC Fellowships. Smith is recognized for his work on efficient and parallel large-scale sparse tensor factorization for machine learning applications. You is recognized for his work on designing accurate, fast, and scalable machine learning algorithms on distributed systems.
Shaden Smith’s research is in the general area of parallel and high performance computing with a special focus on developing algorithms for sparse tensor factorization. Sparse tensor factorization seeks to address the problem of achieving parallel efficiency with sets of large and/or irregular data.
Smith has made several fundamental contributions that have already advanced the state of the art on sparse tensor factorization and completion algorithms. For example, he developed serial and parallel algorithms in the area of Canonical Polyadic Decomposition (CPD) that require 33 percent fewer operations than the best previously known approaches. He also developed algorithms for Tucker decompositions that are 21 times faster and require 28 times less memory than existing algorithms. Smith’s algorithms can efficiently operate on systems containing a small number of multi-core/manycore processors to systems containing tens of thousands of cores.
Yang You’s research interests include scalable algorithms, parallel computing, distributed systems and machine learning. As computers increasingly use more time and energy to transfer data (i.e., communicate), the invention or identification of algorithms that reduce communication within systems is becoming increasingly essential. In well-received research papers, You has made several fundamental contributions that reduce the communications between levels of a memory hierarchy or between processors over a network.
In his most recent work, “Scaling Deep Learning on GPU and Knights Landing Clusters,” Yang’s goal is to scale up training of neural nets so relatively slow networks can be redesigned for high performance clusters. This approach has reduced the percentage of communication from 87 percent to 14 percent and resulted in a five-fold increase in the speed of the data transfer.
The ACM/IEEE-CS George Michael Memorial HPC (GMM) Fellowship is endowed in memory of George Michael, one of the founding fathers of the SC Conference series. The fellowship honors exceptional PhD students throughout the world whose research focus is on high performance computing applications, networking, storage or large-scale data analytics using the most powerful computers that are currently available. The Fellowship includes a $5,000 honorarium and travel expenses to attend SC17 in Denver Colorado, November 13–16, 2017, where the GMM Fellowships will be formally presented.