ACM Home Page
Please provide us with feedback. Feedback
Enabling scalability and performance in a large scale CMP environment
Full text PdfPdf (249 KB)
Source European Conference on Computer Systems archive
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007 table of contents
Lisbon, Portugal
SESSION: Multi-core/multi-processor issues table of contents
Pages: 73 - 86  
Year of Publication: 2007
ISBN ~ ISSN:0163-5980 , 978-1-59593-636-3
Also published in ...
Authors
Bratin Saha  Intel Corporation
Ali-Reza Adl-Tabatabai  Intel Corporation
Anwar Ghuloum  Intel Corporation
Mohan Rajagopalan  Intel Corporation
Richard L. Hudson  Intel Corporation
Leaf Petersen  Intel Corporation
Vijay Menon  Intel Corporation
Brian Murphy  Intel Corporation
Tatiana Shpeisman  Intel Corporation
Eric Sprangle  Intel Corporation
Anwar Rohillah  Intel Corporation
Doug Carmean  Intel Corporation
Jesse Fang  Intel Corporation
Sponsor
SIGOPS: ACM Special Interest Group on Operating Systems
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 30,   Downloads (12 Months): 293,   Citation Count: 5
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1272996.1273006
What is a DOI?

ABSTRACT

Hardware trends suggest that large-scale CMP architectures, with tens to hundreds of processing cores on a single piece of silicon, are iminent within the next decade. While existing CMP machines have traditionally been handled in the same way as SMPs, this magnitude of parallelism introduces several fundamental challenges at the architectural level and this, in turn, translates to novel challenges in the design of the software stack for these platforms. This paper presents the "Many Core Run Time" (McRT), a software prototype of an integrated language runtime that was designed to explore configurations of the software stack for enabling performance and scalability on large scale CMP platforms. This paper presents the architecture of McRT and discusses our experiences with the system, including experimental evaluation that lead to several interesting, non-intuitive findings, providing key insights about the structure of the system stack at this scale. A key contribution of this paper is to demonstrate how McRT enables near linear improvements in performance and scalability for desktop workloads such as the popular XviD encoder and a set of RMS (recognition, mining, and synthesis) applications. Another key contribution of this work is its use of McRT to explore non-traditional system configurations such as a light-weight executive in which McRT runs on "bare metal" and replaces the traditional OS. Such configurations are becoming an increasingly attractive alternative to leverage heterogeneous computing uints as seen in today's CPU-GPU configurations.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
 
2
 
3
Next Generation POSIX Threading. http://www-124.ibm.com/pthreads/
 
4
U. Drepper, and I. Molnar. The native POSIX thread library for Linux, Jan 2003. http://people.redhat.com/drepper/nptl-design.pdf.
 
5
D. Vianney, Hyper-Threading speeds Linux, Jan 2003. http://www-128.ibm.com/developerworks/linux/library/l-htl/
 
6
Microsoft Corp, Windows Support for Hyper-Threading technology, 2002. download.microsoft.com/download/5/7/7/577a5684-8a83-43ae-9272-ff260a9c20e2/Hyper-thread_Windows.doc
7
8
 
9
N. Nagarajaya, Improving Application Efficiency Through Chip Multi-Threading, Sun Developer Network, Mar 2005. developers.sun.com/solaris/articles/chip_multi_thread.html
 
10
11
 
12
S. Gribble, M. Welsh, R. von Behren, E. Brewer, D. Culler, N. Borisov, S. Czerwinski, R. Gummadi, J. Hill, A. Josheph, R. Katz, Z. Mao, S. Ross, and B. Zhao. The Ninja Architecture for Robust Internet-Scale Systems and Services. Sp. lss.: Computer Networks on Pervasive Computing 2000.
13
14
15
16
 
17
18
 
19
20
21
 
22
The K42 project, IBM Research. http://www.research.ibm.com/k42/
 
23
The K42/Tornado Operating System. http://www.eecg.toronto.edu/~tornado/
 
24
T. G. Mattson and G. Henry. An overview of the Intel TFLOPS supercomputer. Intel Technology Journal, 1, 1998.
 
25
Sharad Garg, Robert Godley, Richard Griffiths, Andrew Pfiffer, Terry Prickett, David Robboy, Stan Smith, T. Mack Stallcup, and Stephen Zeisset. Achieving large scale parallelism through operating system resource management on the Intel TFLOPS supercomputer. Intel Technology Journal, 1st quarter 1998.
 
26
Ron Brightwell, Rolf Riesen, Keith D. Underwood, Trammell Hudson, Patrick G. Bridges, Arthur B. Maccabe: A Performance Comparison of Linux and a Lightweight Kernel. CLUSTER 2003: 251--258
 
27
IBM Research Hypervisor. http://www.research.ibm.com/hypervisor/.
 
28
Boris Dragovic, Keir Fraser, Steve Hand, Tim Harris, Alex Ho, Ian Pratt, Andrew Warfield, Paul Barham, and Rolf Neugebauer. Xen and the Art of Virtualization. SOSP, 2003.
29
30
 
31
 
32
J. Rattner. Platform 2015. Intel Dev. Forum, Spring 2005.
 
33
J. Rattner. Tera-Scale Research Program. Intel Dev. Forum, Spring 2006.
 
34
35
36
37
38
 
39
 
40
 
41
P. Dubey. Recognition, Mining, and Synthesis moves computers to the era of tera. Technology@Intel, Feb 2005.
 
42
Craig, T. S. Building FIFO and priority-queueing spin locks from atomic swap. Technical Report TR 93-02-02, Dept of Computer Science, University of Washington, Feb. 1993.
 
43
44
 
45
 
46
 
47
B. So, A. M. Ghuloum, Y. Wu. Optimizing data parallel operations on many-core platforms. STMCS 2006.
 
48
49
50
 
51
IA-32 Intel Architecture Software Developer's Manual. Intel Corporation.
 
52
Bradski, G.; Kaehler, A.; Pisarevsky, V. "Learning-Based Computer Vision with Intel's Open Source Computer Vision Library." Intel Technology Journal. http://developer.intel.com/technology/itj/2005/volume09issue02/art03_learning_vision/p01_abstract.htm. May 2005.
53


Collaborative Colleagues:
Bratin Saha: colleagues
Ali-Reza Adl-Tabatabai: colleagues
Anwar Ghuloum: colleagues
Mohan Rajagopalan: colleagues
Richard L. Hudson: colleagues
Leaf Petersen: colleagues
Vijay Menon: colleagues
Brian Murphy: colleagues
Tatiana Shpeisman: colleagues
Eric Sprangle: colleagues
Anwar Rohillah: colleagues
Doug Carmean: colleagues
Jesse Fang: colleagues