Emerging accelerator platforms for data centers

CPU and GPU platforms may not be the best options for many emerging compute patterns, which led to a new breed of emerging accelerator platforms. This article gives a comprehensive overview with a focus on commercial platforms

Quantifying the latency benefits of near-edge and in-network FPGA acceleration

Transmitting data to cloud datacenters in distributed IoT applications introduces significant communication latency, but is often the only feasible solution when source nodes are computationally limited. To address latency concerns, cloudlets, in-network computing, and more capable edge nodes are all being explored as a way of moving processing capability towards the edge of the network. Hardware acceleration using Field Programmable Gate Arrays (FPGAs) is also seeing increased interest due to reduced computation latency and improved efficiency. This paper evaluates the the implications of these offloading approaches using a case study neural network based image classification application, quantifying both the computation and communication latency resulting from different platform choices. We consider communication latency including the ingestion of packets for processing on the target platform, showing that this varies significantly with the choice of platform. We demonstrate that emerging in-network accelerator approaches offer much improved and predictable performance as well as better scaling to support multiple data sources

Oregon Capital Scan: A Line is Drawn

This report was written with the primary intention of helping to educate entrepreneurs and growth company leaders with respect to the variety and scale of capital sources currently available in the State. It is not uncommon for those seeking to enter the capital markets as an entrepreneur to possess a limited understanding of where capital can be found. This knowledge gap leads to a level of perceived risk uncertainty that inhibits company formation and growth. This report is not intended to instruct entrepreneurs in the skills required to secure funding, rather it is intended as a catalog of source data to enlighten as to the many different types of capital available. This report is also intended for policy makers and those who work to support the development of a thriving growth company ecosystem in the State. This includes the sponsors of this report who seek to find new ways to bring together education and resources to enhance the ability of those who choose to build their companies in Oregon. This report can serve as a baseline of quantitative data that may help everyone to better understand where we are as a State now, with respect to the key ingredient of growth capital, and help us measure our progress and improvements over time

ASCR/HEP Exascale Requirements Review Report

This draft report summarizes and details the findings, results, and recommendations derived from the ASCR/HEP Exascale Requirements Review meeting held in June, 2015. The main conclusions are as follows. 1) Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude -- and in some cases greater -- than that available currently. 2) The growth rate of data produced by simulations is overwhelming the current ability, of both facilities and researchers, to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. 3) Data rates and volumes from HEP experimental facilities are also straining the ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. 4) A close integration of HPC simulation and data analysis will aid greatly in interpreting results from HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. 5) Long-range planning between HEP and ASCR will be required to meet HEP's research needs. To best use ASCR HPC resources the experimental HEP program needs a) an established long-term plan for access to ASCR computational and data resources, b) an ability to map workflows onto HPC resources, c) the ability for ASCR facilities to accommodate workflows run by collaborations that can have thousands of individual members, d) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, e) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio