2.5D FPGA-HBM Integration Challenges

2017 ◽  
Vol 2017 (1) ◽  
pp. 000336-000341 ◽  
Author(s):  
Jaspreet Gandhi ◽  
Boon Ang ◽  
Tom Lee ◽  
Henley Liu ◽  
Myongseob Kim ◽  
...  
Keyword(s):  
Data Center ◽  
High Performance ◽  
Point Of View ◽  
Cloud Services ◽  
Lower Power ◽  
High Bandwidth ◽  
3D Stacking ◽  
Interconnect Technology ◽  
Performance Computing ◽  
Scale Data

Abstract FPGA partitioning and high density integration using interposer based 2.5D stacked silicon interconnect technology (SSIT) has been the pioneering work at Xilinx for several years enabling advanced applications in high performance computing, networking, hyper scale data center and cloud services etc. With the insatiable demand for acceleration workloads, FPGAs need to be coupled with in package memory to enable higher bandwidth, lower power and smaller form factor architecture. 3D stacking based high bandwidth memory (HBM) has paved the way to realize such applications providing 10X higher bandwidth, 4X lower power vs DDR4. This paper provides an overview of unique challenges involved with 2.5D FPGA-HBM SSIT integration from design, process/package development, test and reliability point of view

High Performance Computing for Analyzing PB-Scale Data in Nuclear Experiments and Simulations

2011 ◽  
pp. 107-117
Author(s):  
Takayuki Tatekawa ◽  
Naoya Teshima ◽  
Noriyuki Kushida ◽  
Hiroko Nakamura Miyamura ◽  
Guehee Kim ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Performance Computing ◽  
Scale Data

scholarly journals A Framework for HI Spectral Source Finding Using Distributed-Memory Supercomputing

2014 ◽  
Vol 31 ◽  
Author(s):  
Stefan Westerlund ◽  
Christopher Harris
Keyword(s):  
High Performance ◽  
Distributed Memory ◽  
Computing Systems ◽  
Sky Surveys ◽  
Local Statistics ◽  
Wide Range ◽  
Gaussian Source ◽  
High Bandwidth ◽  
Traditional Approaches ◽  
Performance Computing

AbstractThe latest generation of radio astronomy interferometers will conduct all sky surveys with data products consisting of petabytes of spectral line data. Traditional approaches to identifying and parameterising the astrophysical sources within this data will not scale to datasets of this magnitude, since the performance of workstations will not keep up with the real-time generation of data. For this reason, it is necessary to employ high performance computing systems consisting of a large number of processors connected by a high-bandwidth network. In order to make use of such supercomputers substantial modifications must be made to serial source finding code. To ease the transition, this work presents the Scalable Source Finder Framework, a framework providing storage access, networking communication and data composition functionality, which can support a wide range of source finding algorithms provided they can be applied to subsets of the entire image. Additionally, the Parallel Gaussian Source Finder was implemented using SSoFF, utilising Gaussian filters, thresholding, and local statistics. PGSF was able to search on a 256GB simulated dataset in under 24 minutes, significantly less than the 8 to 12 hour observation that would generate such a dataset.

scholarly journals ScalaLab and GroovyLab: Comparing Scala and Groovy for Scientific Computing

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Stergios Papadimitriou ◽  
Kirsten Schwark ◽  
Seferina Mavroudi ◽  
Kostas Theofilatos ◽  
Spiridon Likothanasis
Keyword(s):  
Programming Language ◽  
User Interfaces ◽  
High Performance ◽  
Scientific Computing ◽  
Point Of View ◽  
Java Virtual Machine ◽  
Programming Environment ◽  
Fast Pace ◽  
Native Code ◽  
Performance Computing

ScalaLab and GroovyLab are both MATLAB-like environments for the Java Virtual Machine. ScalaLab is based on the Scala programming language and GroovyLab is based on the Groovy programming language. They present similar user interfaces and functionality to the user. They also share the same set of Java scientific libraries and of native code libraries. From the programmer's point of view though, they have significant differences. This paper compares some aspects of the two environments and highlights some of the strengths and weaknesses of Scala versus Groovy for scientific computing. The discussion also examines some aspects of the dilemma of using dynamic typing versus static typing for scientific programming. The performance of the Java platform is continuously improved at a fast pace. Today Java can effectively support demanding high-performance computing and scales well on multicore platforms. Thus, both systems can challenge the performance of the traditional C/C++/Fortran scientific code with an easier to use and more productive programming environment.

scholarly journals Enabling Large-Scale Biomedical Analysis in the Cloud

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Ying-Chih Lin ◽  
Chin-Sheng Yu ◽  
Yen-Jen Lin
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Computing System ◽  
Biomedical Data ◽  
Data Intensive Computing ◽  
Biomedical Analysis ◽  
Data Intensive ◽  
Large Scale Data ◽  
Performance Computing ◽  
Scale Data

Recent progress in high-throughput instrumentations has led to an astonishing growth in both volume and complexity of biomedical data collected from various sources. The planet-size data brings serious challenges to the storage and computing technologies. Cloud computing is an alternative to crack the nut because it gives concurrent consideration to enable storage and high-performance computing on large-scale data. This work briefly introduces the data intensive computing system and summarizes existing cloud-based resources in bioinformatics. These developments and applications would facilitate biomedical research to make the vast amount of diversification data meaningful and usable.

scholarly journals Design of 2.5D Interposer in High Bandwidth Memory and Through Silicon Via for High Speed Signal

2020 ◽  
Author(s):  
Bo Pu
Keyword(s):  
Artificial Intelligence ◽  
High Speed ◽  
High Performance ◽  
Equivalent Model ◽  
Electrical Characteristics ◽  
Through Silicon Via ◽  
High Bandwidth ◽  
Signal Path ◽  
High Speed Switching ◽  
Performance Computing

<p>The 2.5D interposer becomes a crucial solution to realize grand bandwidth of HBM for the increasing data requirement of high performance computing (HPC) and Artificial Intelligence (AI) applications. To overcome high speed switching bottleneck caused by the large resistive and capacitive characteristics of interposer, design methods to achieve an optimized performance in a limited routing area are proposed. Unlike the conventional single through silicon via (TSV), considering the reliability, multiple TSV are used as the robust 3D interconnects for each signal path. An equivalent model to accurately describe the electrical characteristics of the multiple TSVs, and a configuration pattern strategy of TSV to mitigate crosstalk are also proposed.</p>

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