Potential Project.

Slides:



Advertisements
Similar presentations
Yaron Doweck Yael Einziger Supervisor: Mike Sumszyk Spring 2011 Semester Project.
Advertisements

Scalable Multi-Cache Simulation Using GPUs Michael Moeng Sangyeun Cho Rami Melhem University of Pittsburgh.
Optimization on Kepler Zehuan Wang
GPU Virtualization Support in Cloud System Ching-Chi Lin Institute of Information Science, Academia Sinica Department of Computer Science and Information.
Threads. Objectives To introduce the notion of a thread — a fundamental unit of CPU utilization that forms the basis of multithreaded computer systems.
Programming with CUDA, WS09 Waqar Saleem, Jens Müller Programming with CUDA and Parallel Algorithms Waqar Saleem Jens Müller.
Disco Running Commodity Operating Systems on Scalable Multiprocessors.
Module 8: Monitoring SQL Server for Performance. Overview Why to Monitor SQL Server Performance Monitoring and Tuning Tools for Monitoring SQL Server.
To GPU Synchronize or Not GPU Synchronize? Wu-chun Feng and Shucai Xiao Department of Computer Science, Department of Electrical and Computer Engineering,
Operating Systems Should Manage Accelerators Sankaralingam Panneerselvam Michael M. Swift Computer Sciences Department University of Wisconsin, Madison,
1 Integrating GPUs into Condor Timothy Blattner Marquette University Milwaukee, WI April 22, 2009.
Scalable Data Clustering with GPUs Andrew D. Pangborn Thesis Defense Rochester Institute of Technology Computer Engineering Department Friday, May 14 th.
WORK ON CLUSTER HYBRILIT E. Aleksandrov 1, D. Belyakov 1, M. Matveev 1, M. Vala 1,2 1 Joint Institute for nuclear research, LIT, Russia 2 Institute for.
General Purpose Computing on Graphics Processing Units: Optimization Strategy Henry Au Space and Naval Warfare Center Pacific 09/12/12.
Taking the Complexity out of Cluster Computing Vendor Update HPC User Forum Arend Dittmer Director Product Management HPC April,
Parallel Computers Organizations and Architecture Department of Computer Science Southern Illinois University Edwardsville Summer, 2015 Dr. Hiroshi Fujinoki.
Copyright © George Coulouris, Jean Dollimore, Tim Kindberg This material is made available for private study and for direct.
1 MAIN TABLE OF CONTENTS Definition: SOFTWARE AGENT HOW MANY TYPES OF AGENT? DEFINITION OF MOBILE AGENT: SOFTWARE AGENTS PROPERTIES, WORKING OF MOBILE.
Yuanyuan ZhouUIUC-CS Challenges and Opportunities for OS in the Multi-Core Era Yuanyuan (YY) Zhou University of Illinois at Urbana-Champaign
GPU Architecture and Programming
Some key aspects of NVIDIA GPUs and CUDA. Silicon Usage.
SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING | SCHOOL OF COMPUTER SCIENCE | GEORGIA INSTITUTE OF TECHNOLOGY MANIFOLD Manifold Execution Model and System.
An Execution Model for Heterogeneous Multicore Architectures Gregory Diamos, Andrew Kerr, and Sudhakar Yalamanchili Computer Architecture and Systems Laboratory.
Authors: Danhua Guo 、 Guangdeng Liao 、 Laxmi N. Bhuyan 、 Bin Liu 、 Jianxun Jason Ding Conf. : The 4th ACM/IEEE Symposium on Architectures for Networking.
Introduction to CUDA CAP 4730 Spring 2012 Tushar Athawale.
Shangkar Mayanglambam, Allen D. Malony, Matthew J. Sottile Computer and Information Science Department Performance.
Shouqing Hao Institute of Computing Technology, Chinese Academy of Sciences Processes Scheduling on Heterogeneous Multi-core Architecture.
Computer Architecture Lecture 24 Parallel Processing Ralph Grishman November 2015 NYU.
3/12/2013Computer Engg, IIT(BHU)1 CUDA-3. GPGPU ● General Purpose computation using GPU in applications other than 3D graphics – GPU accelerates critical.
1 Pertemuan 3 Operating Cisco IOS Software. Discussion Topics The purpose of Cisco IOS software Router user interface Router user interface modes Cisco.
My Coordinates Office EM G.27 contact time:
Scaling up R computation with high performance computing resources.
ATCA based LLRF system design review DESY Control servers for ATCA based LLRF system Piotr Pucyk - DESY, Warsaw University of Technology Jaroslaw.
Heterogeneous Processing KYLE ADAMSKI. Overview What is heterogeneous processing? Why it is necessary Issues with heterogeneity CPU’s vs. GPU’s Heterogeneous.
Matthew Royle Supervisor: Prof Shaun Bangay.  How do we implement OpenCL for CPUs  Differences in parallel architectures  Is our CPU implementation.
Lecture 5. Example for periority The average waiting time : = 41/5= 8.2.
Introduction to threads
NFV Compute Acceleration APIs and Evaluation
Gwangsun Kim, Jiyun Jeong, John Kim
CS427 Multicore Architecture and Parallel Computing
The Multikernel: A New OS Architecture for Scalable Multicore Systems
Tracing and Performance Analysis Tools for Heterogeneous Multicore System by Soon Thean Siew.
Parallel Computing Lecture
Chapter 4: Multithreaded Programming
Linux Operating System Architecture
Performance Tuning Team Chia-heng Tu June 30, 2009
NVIDIA Profiler’s Guide
Implementation of Efficient Check-pointing and Restart on CPU - GPU
Chapter 4: Threads.
CS 286 Computer Organization and Architecture
Operating System Concepts
Chapter 4: Threads.
Chapter 4 Multithreading programming
Faster File matching using GPGPU’s Deephan Mohan Professor: Dr
Presented by Remzi Can Aksoy
Chapter 4: Threads.
PerfView Measure and Improve Your App’s Performance for Free
NVIDIA Fermi Architecture
Modified by H. Schulzrinne 02/15/10 Chapter 4: Threads.
CGS 3763 Operating Systems Concepts Spring 2013
Mid Term review CSC345.
Tools.
B.Ramamurthy Chapter 2 : Appendix
Multithreaded Programming
Tools.
Introduction to Heterogeneous Parallel Computing
Software Acceleration in Hybrid Systems Xiaoqiao (XQ) Meng IBM T. J
Operating Systems: A Modern Perspective, Chapter 3
Presentation transcript:

Potential Project

samba TCP/IP offload Data copy Multicore scalability TCP buffer to Samba buffer (reduce) Multicore scalability Workload generation (IOmeter) Performance Monitoring

Windows research kernel Scholarship Example topics User mode scheduling of CPUs User mode cross-process thread migration Cross-process object invocation User mode file system Continyations and kernel stack sharing

Performance tools Applications System software Architecture Profiling, Tracing, Modeling System software Profiling & Modeling Architecture Simulation, Modeling Scratchpad, multicore, power, verilog2sc

Parallel computing Playstation3 GPGPU PAC DSP 9-core, (PowerPC, GPU, 7SPUs) GPGPU nvidia PAC DSP System software, microkernel+IPC Multimedia & Security Application (g-phone/android, vlc) Programming and execution model