The July 2015 issue of the Association for Computing Machinery’s magazine focuses on exascale computing and big data and features a video of and paper by University of Iowa Vice President for Research and Economic Development Daniel A. Reed.

Daniel A. Reed
The Communications of the ACM video may be found online at and the article may be found at Reed coauthored the paper with colleague Jack Dongarra, who holds appointments at the University of Tennessee’s Oak Ridge National Laboratory and the University of Manchester.

Reed, former Corporate Vice President at Microsoft and an ACM Fellow since 2003, is a leader in higher education and expert on the science and public policy dimensions of high-performance computing, the Internet and other technologies, the intersection of advanced computing and scientific discovery, and the role of universities in building the knowledge economy. In addition to his role as vice president, Reed is Chair of the Computational Science and Bioinformatics program and Professor of Computer Science, Electrical and Computer Engineering and Medicine at the University of Iowa.

Exascale computing refers to computing systems capable of at least one exaFLOPS, or a billion billion calculations per second. Such capacity represents a thousand-fold increase over the first petascale computer that came into operation in 2008.

Big data is a term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy.

In the paper, the basis for the related video, Reed and Dongarra posit that the tools and cultures of high-performance computing and big data analytics have diverged, to the detriment of both, and that they need to come together to effectively address a range of major research areas.

They note that there are many challenges on the road to ever more advanced computing, including “system power consumption and environmentally friendly cooling, massive parallelism, and component failures, data and transaction consistency, metadata and ontology management, precision and recall at scale, and multidisciplinary data fusion and preservation.”

At the same time, they say, advanced computing systems need to be useable and accessible to researchers from a wide range of disciplines who may not be computer experts themselves.

Reed and Dongrarra also argue that, given the international nature of science, new and advanced computer architectures and global standards for processing data are needed to move research forward.

“There are great opportunities and great challenges in advanced computing, in both computation and data analysis,” they write in their conclusion. “Scientific discovery via computational science and data analytics is truly the ‘endless frontier’ about which Vannevar Bush [an American engineer, inventor and science administrator who headed the U.S. Office of Scientific Research and Development during World War II] spoke so eloquently in 1945. The challenges are for all of computer science to sustain the research, development, and deployment of the high-performance computing infrastructure needed to enable those discoveries.”

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