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A Unified, Low-overhead Framework to Support Continuous Profiling and Optimization Xubin (Ben) He Storage Technology & Architecture Research(STAR)

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Presentation on theme: "A Unified, Low-overhead Framework to Support Continuous Profiling and Optimization Xubin (Ben) He Storage Technology & Architecture Research(STAR)"— Presentation transcript:

1 A Unified, Low-overhead Framework to Support Continuous Profiling and Optimization Xubin (Ben) He (hexb@tntech.edu) Storage Technology & Architecture Research(STAR) Lab Department of Electrical and Computer Engineering 22nd IEEE International Performance Computing and Communications Conference (IPCCC’2003)

2 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 2 Outline Introduction Architecture and Design Performance Evaluations Conclusions and Future Work

3 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 3 Introduction

4 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 4 Motivations System profiling is an important mechanism to observe system activities. Profiling-based optimization has become a key technique. Continuous and online optimization is needed because of changed system usage patterns.

5 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 5 Current State-of-the-art Traditional approaches bring high overhead to already overloaded system. Profiling and optimization overhead: Raw Data Gathering Data Recording Data Processing Feedback

6 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 6 Introducing Unified, Low-overhead Framework (ULF) Offload computing overheads from host processors to an embedded processor; Continuous feedback loop model: 1. Low overhead profiling to gather system event data; 2. Parallel processing raw data and policy generation; 3. Apply policy to host; Host ULF 1 2 3

7 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 7 Introduction Architecture and Design Performance Evaluations Conclusions and Future Work

8 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 8 Components ULF board: an embedded processor with a sufficient amount of RAM Host-side module: APIs as a library or kernel module Board-side module:embedded os, a libray, plug-ins

9 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 9 ULF Board Low cost, low power embedded processor. Expandable with secondary PCI slot. Interface with host via standard PCI slot

10 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 10 Interaction between Plug-ins and Boards Initial stage-->Running--->Cleanup

11 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 11 Example Applications Low overhead profiling On-line program optimizer On-line file system cache optimizer …

12 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 12 Introduction Architecture and Design Performance Evaluaitons Conclusions and Future Work

13 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 13 Experimental Setup Methodology  Prototype using Intel IOP310 processor, Linux 2.4.16  I/O profiling tool: LTT(Linux Trace Toolkit) Workloads  Postmark of Network Appliances: throughput  20k initial files, transactions ranging from 150k to 300k.  Iozone 4 configurations 1)NTNR: Neither Traced Nor Recorded 2)TNR: Traced but Not Recorded 3)TDR: Traced and Disk Recorded 4)TFR: Traced and ULF Recorded

14 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 14 PostMark Results

15 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 15 Different W/R ratio

16 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 16 Iozone results

17 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 17 Introduction Architecture and Design Performance Evaluations Conclusions and Future Work

18 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 18 Conclusions A unified, low-overhead framework helps profiling tools to save profiling data rapidly and perform run-time parallel processing. Reduces profiling overhead LTT: 40%-->0.4%.

19 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 19 Future Work Apply ULF to more case studies Performance: Adaptively adjust system prefetching and caching policy; Online code rewrite and recompilation; Security: Monitor abnormal system access and high risk events. Intrusion detection

20 ULF Storage Technology & Architecture Research lab(STAR) T.T.U 20 Acknowledgements Dr. Ken Yang Ming Zhang NSF Manufacturing Center at T.T.U

21 Xubin He (hexb@tntech.edu) http://www.ece.tntech.edu/hexb/starlab.htm Storage Technology & Architecture Research(STAR) Lab Department of Electrical and Computer Engineering A Unified, Low-overhead Framework to Support Continuous Profiling and Optimization IPCCC’2003


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