Abstract
This paper describes the vision behind and the mission of the Maxeler Application Gallery (AppGallery.Maxeler.com) project. First, it concentrates on the essence and performance advantages of the Maxeler dataflow approach. Second, it reviews the support technologies that enable the dataflow approach to achieve its maximum. Third, selected examples of the Maxeler Application Gallery are presented; these examples are treated as the final achievement made possible when all the support technologies are put to work together (internal infrastructure of the AppGallery.Maxeler.com is given in a follow-up paper). As last, the possible impact of the Application Gallery is presented and the major conclusions are drawn.
Highlights
A rule is that each and every paradigm-shift idea passes through four phases in its lifetime
This paper describes the vision behind and the mission of the Maxeler Application Gallery (AppGallery.Maxeler.com) project
Selected examples of the Maxeler Application Gallery are presented; these examples are treated as the final achievement made possible when all the support technologies are put to work together
Summary
A rule is that each and every paradigm-shift idea passes through four phases in its lifetime. The Maxeler Application Gallery project concentrates on the dataflow approach that accelerates critical loops by forming customized execution graphs that map onto an reconfigurable infrastructure (currently FPGA-based) This approach provides considerable speedups over the existing control flow approaches, unprecedented power savings, as well as a significant size reduction of the overall supercomputer. The dataflow supercomputing paradigm essence At the time when the von Neumann paradigm for computing was formed, the technology was such that the ratio of arithmetic or logic (ALU) operation latencies over the communication (COMM) delays to memory or another processor t(ALU)/t(COMM) was extremely large (sometime argued to be approaching infinity) In his famous lecture notes on Computing, the Nobel Laureate Richard Feynman presented an observation that in theory, ALU operations could be done with zero energy, while communications can never reach zero energy levels, and that speed and energy of computing could be traded. The presentation template, wherever possible, conditionally speaking, includes the following elements: (a) The whereabouts (b) The algorithm implementation essence supported with a figure, (c) The coding approach changes made necessary by the paradigm changes, supported by GitHub details, (d) The major highlights, supported with a figure, (e) The advantages and drawbacks, (f ) The future trends, and (g) Conclusions related to complexity, performance, energy, and risks leading to possible problems in the domains of complexity, performance, energy, and creation of new risks that recursively open a new round of all the above
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