Designing Tomorrow’s Computing Platforms Challenges, Solutions, and Tools

1. Designing Tomorrow’s Computing PlatformsChallenges, Solutions, and Tools Sudhanva Gurumurthi e-mail: gurumurthi@cs.virginia.edu

2. Talk Outline • Modern Computer Architecture • The Good • The Bad • The Ugly • My Previous Work • Current and Future Research

3. The Good

4. Source: http://www.intel.com/technology/silicon/mooreslaw/

5. Microprocessor Technology Advancement • Plentiful Transistors • Superscalar, SMT, CMP • Larger caches, deeper memory-hierarchy • High-bandwidth access to memory • Simultaneously, clock frequencies have grown tremendously

6. Storage Has Become Ubiquitous Density Speed

7. Growth in Drive Performance Source: Hitachi GST Technology Overview Charts, http://www.hitachigst.com/hdd/technolo/overview/storagetechchart.html

8. The Bad

9. Power Dissipation

10. Particle Induced Soft-Errors 0 1 Source: FACT Group, Intel

11. Are you kidding me? • No!! • In 2000, Sun Microsystems reported random crashes in one of its server products due to no parity-protection in the caches. • Eugene Normand’s study of the error-logs of large systems indicated several such errors • There are conference sessions and even full conferences/workshops devoted to this problem • Have personal experience collecting and analyzing soft-error data

12. Where Do These Particles Come From? • Neutrons • Terrestrial cosmic rays • Alpha particles • Packaging

13. Should we worry? • Yes!! • Thanks to Moore’s Law • Lower operating voltages • Exponential increase transistor integration density • Power management (voltage-scaling) • Larger systems • Impractical to shield against cosmic rays • Need several feet of concrete • Radiation-hardening hurts performance, area, and cost

14. Redundant Multi-Threading Input Replicator Output Comparator Rest of the System Source: Mukherjee et al, “Detailed Design and Evaluation of Redundant Multithreading Alternatives”, ISCA’02

15. Performance of Redundant Multi-Threading

16. Temperature Affects Disk Drive Reliability • Heat-Related Problems • Data corruption • Higher off-track errors • Head-crashes • Disk drive design constrained by the thermal-envelope • Puts a limit on drive performance Source: D. Anderson et al, “More than an Interface – SCSI vs. ATA”, FAST 2003.

17. Data-Rate Capacity Increase RPM Shrink Platter (Dia)4.6 (RPM)2.8 (# Platters) Temperature Thermal-Constrained Design Data Rate =~ (Linear-Density)*(RPM)*(Diameter) 1 platter Lower Capacity Lower Data Rate 40% Annual IDR Growth Increase RPM Power =~(# Platters)*(RPM)2.8(Diameter)4.6

18. The BadDrive Temperature Thermal-Envelope

19. The BadData Rate

20. 30-60% Performance Boost for 10,000 RPM Increase

21. Search-Engine Thermal Behavior Thermal Envelope = 45.22 C

22. The Ugly

23. Design Tools • Designing complex systems requires extensive simulation • Need to model all aspects of the system • Software layers • Power • Temperature • Effect of faults

24. Simulation Problems • Painfully slow • Speed vs. Accuracy • No good support available for modeling effects like temperature and reliability • Can themselves be hard to write • Buggy

25. My Previous Work

26. Thesis Work:Power Management of Enterprise Storage Systems

27. Enterprise Storage Market Growth • Storage demand growing at annual rate of 60% • By 2008, a company would manage 10 times the storage it has today. Sources: 1. “Enterprise Storage: A Look into the Future”, TNM Seminar Series, Oct. 31, 2000 2. “More Power Needed”, Energy User News, Nov. 2002

28. Power Demands of Data Centers“What matters most to the computer designers at Google is not speed but power – low-power – because data centers can consume as much electricity as a city”, Eric Schmidt, CEO, Google • Data centers consume several Megawatts of power • Electricity bill • $4 billion/year • Disks account for 27% of computing-load costs • Difficult to cool at high power-densities Sources: 1. “Intel’s Huge Bet Turns Iffy”, New York Times article, September 29, 2002 2. “Power, Heat, and Sledgehammer, Apr. 2002. 3. “Heat Density Trends in Data Processing, Computer Systems, and Telecommunications Equipment”, 2000.

29. Data Center Cooling Costs • Data center of a large financial institution in New York City • Power consumption ~ 4.8 MW Source: “Energy Benchmarking and Case Study – NY Data Center No. 2”, Lawrence Berkeley National Lab, July 2003.

30. Where Does Power Go? Active = 11 W Spindle Motor (SPM) Idle = 9 W Standby = 1 W Voice-Coil Motor (VCM) 4 W Seek = 13 W

31. Disk Request Idleness Detected Disk Active Disk Active Idle Spindown Spinup Standby Mode Traditional Power Management (TPM) Time

32. I/O Characteristics of Server Systems • Large number of disks • RAID arrays • Heavier I/O loads sustained over long periods. • Stringent performance requirements. • Server disks physically different • Not made to use spindowns. • Longer spindown/spinup latencies • Server Disk - Hitachi Ultrastar – 15 seconds/26 seconds • Laptop Disk - Hitachi Travelstar – 4.5 seconds

33. Feasibility of Applying TPM • No prior study on how to tackle this problem systematically. • Questions • Is there idleness? • Can we do TPM? • Answers • Yes • No! Why?? • Large number of very short duration (few ms) idle-periods

34. The Solution • Traditional Power Management • Not effective for server workloads • Power =~(# Platters)*(RPM)2.8(Diameter)4.6 • All three can be varied at design-time to meet the power budget • Laptop vs. Server disk • RPM could be varied dynamically • Dynamic RPM (DRPM)

35. Potential Benefits of DRPM

36. Control-Policy Performance

37. Research Impact • The feasibility study [ISPASS’03] started off new research in server disk power management • Active groups: UIUC, Rutgers, UMass, UArizona, Rochester • DRPM paper [ISCA’03] widely cited in architecture and systems conferences like ISCA, HPCA, ASPLOS, SOSP, OSDI • Multi-speed drives starting to appear in the market • Hitachi Deskstar 7K400

38. My Other Work • Microarchitectural Techniques to Enhance Redundant Multi-Threading Performance • Instruction Reuse [ISCA’04] • Soft-Error Data Collection and Analysis from Actual Systems (Intel) • Soft-Error Tolerant Cache Coherence-Protocols (Intel) • Simulator Design • SoftWatt [HPCA’02] • MEMSIM (IBM Research)

39. More Details About My Work • Papers: • S. Gurumurthi et al., Disk Drive Roadmap from the Thermal Perspective: A Case for Dynamic Thermal Management, ISCA 2005. • A. Parashar et al., A Complexity-Effective Approach to ALU Bandwidth Enhancement for Instruction-Level Temporal Redundancy, ISCA 2004. • S. Gurumurthi et al., DRPM: Dynamic Speed Control for Power Management in Server Class Disks, ISCA 2003. • S. Gurumurthi et al., Using Complete Machine Simulation for Software Power Estimation: The SoftWatt Approach, HPCA 2002. • Available via my CS Department homepage.

40. Some Research Directions • Temperature-Aware Storage Systems • Devices • Systems issues • Multi-Dimensional Approach to Fault Tolerance • Tradeoffs between performance, power, reliability • Dynamic adaptation • Microarchitectural Support for Security • Design of accurate and fast simulation tools

41. Research Directions in Storage • Storage architecture is still quite a nascent field • Plenty of research opportunities: • Emerging technologies • MEMS, holographic, molecular storage • New Research Avenues • Security • Application/Content-Awareness • Active disks • Long-term and survivable storage

42. Looking for Students! • Shall be offering a research course in Spring 2006. • Many project opportunities • Contact Information: • E-mail: gurumurthi@cs • Office: 236B, Olsson Hall

43. Divider Slide

44. VCM On VCM Off Approach 1:Seek Throttling T E M P E R A T U R E Thermal-Envelope TIME

45. Results 2-42% reduction in IPC gap (avg. 23%)