Slide 1 Computers for the Post-PC Era David Patterson University of California at Berkeley UC Berkeley IRAM Group UC Berkeley ISTORE Group February 2000

Slide 2 Perspective on Post-PC Era PostPC Era will be driven by 2 technologies: 1) “Gadgets”:Tiny Embedded or Mobile Devices –ubiquitous: in everything –e.g., successor to PDA, cell phone, wearable computers 2) Infrastructure to Support such Devices –e.g., successor to Big Fat Web Servers, Database Servers

Slide 3 Outline 1) Example microprocessor for PostPC gadgets 2) Motivation and the ISTORE project vision –AME: Availability, Maintainability, Evolutionary growth –ISTORE’s research principles –Proposed techniques for achieving AME –Benchmarks for AME Conclusions and future work

Slide 4 New Architecture Directions “…media processing will become the dominant force in computer arch. and microprocessor design.” “...new media-rich applications... involve significant real-time processing of continuous media streams, and make heavy use of vectors of packed 8-, 16-, 32-bit integer and Fl. Pt.” Needs include real-time response, continuous media data types (no temporal locality), fine grain parallelism, coarse grain parallelism, memory bandwidth –“How Multimedia Workloads Will Change Processor Design”, Diefendorff & Dubey, IEEE Computer (9/97)

Slide 5 Intelligent RAM: IRAM Microprocessor & DRAM on a single chip: –10X capacity vs. SRAM –on-chip memory latency 5-10X, bandwidth X –improve energy efficiency 2X-4X (no off-chip bus) –serial I/O 5-10X v. buses –smaller board area/volume IRAM advantages extend to: –a single chip system –a building block for larger systems DRAMDRAM fabfab Proc Bus DRAM I/O $$ Proc L2 $ LogicLogic fabfab Bus DRAM I/O

Slide 6 Revive Vector Architecture Cost: $1M each? Low latency, high BW memory system? Code density? Compilers? Performance? Power/Energy? Limited to scientific applications? Single-chip CMOS MPU/IRAM IRAM Much smaller than VLIW For sale, mature (>20 years) (We retarget Cray compilers) Easy scale speed with technology Parallel to save energy, keep performance Multimedia apps vectorizable too: N*64b, 2N*32b, 4N*16b

Slide 7 V-IRAM1: Low Power v. High Perf. Memory Crossbar Switch M M … M M M … M M M … M M M … M M M … M M M … M … M M … M M M … M M M … M M M … M + Vector Registers x ÷ Load/Store 16K I cache 16K D cache 2-way Superscalar Vector Processor 4 x 64 or 8 x 32 or 16 x 16 4 x 64 Queue Instruction I/O Serial I/O

Slide 8 VIRAM-1: System on a Chip Prototype scheduled for tape-out mid um EDL process 16 MB DRAM, 8 banks MIPS Scalar core and 200 MHz 4 64-bit vector unit 200 MHz MB parallel I/O lines 17x17 mm, 2 Watts 25.6 GB/s memory (6.4 GB/s per direction and per Xbar) 1.6 Gflops (64-bit), 6.4 GOPs (16-bit) C P U +$ I/O 4 Vector Pipes/Lanes Memory (64 Mbits / 8 MBytes) Xbar

Slide 9 Media Kernel Performance

Slide 10 Base-line system comparison All numbers in cycles/pixel MMX and VIS results assume all data in L1 cache

Slide 11 IRAM Chip Challenges Merged Logic-DRAM process Cost: Cost of wafer, Impact on yield, testing cost of logic and DRAM Price: on-chip DRAM v. separate DRAM chips? Delay in transistor speeds, memory cell sizes in Merged process vs. Logic only or DRAM only DRAM block: flexibility via DRAM “compiler” (vary size, width, no. subbanks) vs. fixed block Apps: advantages in memory bandwidth, energy, system size to offset challenges?

Slide 12 Other examples: IBM “Blue Gene” 1 PetaFLOPS in 2005 for $100M? Application: Protein Folding Blue Gene Chip –32 Multithreaded RISC processors + ??MB Embedded DRAM + high speed Network Interface on single 20 x 20 mm chip –1 GFLOPS / processor 2’ x 2’ Board = 64 chips (2K CPUs) Rack = 8 Boards (512 chips,16K CPUs) System = 64 Racks (512 boards,32K chips,1M CPUs) Total 1 million processors in just 2000 sq. ft.

Slide 13 Other examples: Sony Playstation 2 Emotion Engine: 6.2 GFLOPS, 75 million polygons per second (Microprocessor Report, 13:5) –Superscalar MIPS core + vector coprocessor + graphics/DRAM –Claim: “Toy Story” realism brought to games

Slide 14 Outline 1) Example microprocessor for PostPC gadgets 2) Motivation and the ISTORE project vision –AME: Availability, Maintainability, Evolutionary growth –ISTORE’s research principles –Proposed techniques for achieving AME –Benchmarks for AME Conclusions and future work

Slide 15 The problem space: big data Big demand for enormous amounts of data –today: high-end enterprise and Internet applications »enterprise decision-support, data mining databases »online applications: e-commerce, mail, web, archives –future: infrastructure services, richer data »computational & storage back-ends for mobile devices »more multimedia content »more use of historical data to provide better services Today’s SMP server designs can’t easily scale Bigger scaling problems than performance!

Slide 16 Lampson: Systems Challenges Systems that work –Meeting their specs –Always available –Adapting to changing environment –Evolving while they run –Made from unreliable components –Growing without practical limit Credible simulations or analysis Writing good specs Testing Performance –Understanding when it doesn’t matter “Computer Systems Research -Past and Future” Keynote address, 17th SOSP, Dec Butler Lampson Microsoft

Slide 17 Hennessy: What Should the “New World” Focus Be? Availability –Both appliance & service Maintainability –Two functions: »Enhancing availability by preventing failure »Ease of SW and HW upgrades Scalability –Especially of service Cost –per device and per service transaction Performance –Remains important, but its not SPECint “Back to the Future: Time to Return to Longstanding Problems in Computer Systems?” Keynote address, FCRC, May 1999 John Hennessy Stanford

Slide 18 The real scalability problems: AME Availability –systems should continue to meet quality of service goals despite hardware and software failures Maintainability –systems should require only minimal ongoing human administration, regardless of scale or complexity Evolutionary Growth –systems should evolve gracefully in terms of performance, maintainability, and availability as they are grown/upgraded/expanded These are problems at today’s scales, and will only get worse as systems grow

Slide 19 The ISTORE project vision Our goal: develop principles and investigate hardware/software techniques for building storage-based server systems that: –are highly available –require minimal maintenance –robustly handle evolutionary growth –are scalable to O(10000) nodes

Slide 20 Principles for achieving AME (1) No single points of failure Redundancy everywhere Performance robustness is more important than peak performance –“performance robustness” implies that real-world performance is comparable to best-case performance Performance can be sacrificed for improvements in AME –resources should be dedicated to AME »compare: biological systems spend > 50% of resources on maintenance –can make up performance by scaling system

Slide 21 Principles for achieving AME (2) Introspection –reactive techniques to detect and adapt to failures, workload variations, and system evolution –proactive techniques to anticipate and avert problems before they happen

Slide 22 Outline 1) Example microprocessor for PostPC gadgets 2) Motivation and the ISTORE project vision –AME: Availability, Maintainability, Evolutionary growth –ISTORE’s research principles –Proposed techniques for achieving AME –Benchmarks for AME Conclusions and future work

Slide 23 Hardware techniques Fully shared-nothing cluster organization –truly scalable architecture –architecture that tolerates partial failure –automatic hardware redundancy

Slide 24 Hardware techniques (2) No Central Processor Unit: distribute processing with storage –Serial lines, switches also growing with Moore’s Law; less need today to centralize vs. bus oriented systems –Most storage servers limited by speed of CPUs; why does this make sense? –Why not amortize sheet metal, power, cooling infrastructure for disk to add processor, memory, and network? –If AME is important, must provide resources to be used to help AME: local processors responsible for health and maintenance of their storage

Slide 25 Hardware techniques (3) Heavily instrumented hardware –sensors for temp, vibration, humidity, power, intrusion –helps detect environmental problems before they can affect system integrity Independent diagnostic processor on each node –provides remote control of power, remote console access to the node, selection of node boot code –collects, stores, processes environmental data for abnormalities –non-volatile “flight recorder” functionality –all diagnostic processors connected via independent diagnostic network

Slide 26 Hardware techniques (4) On-demand network partitioning/isolation –Internet applications must remain available despite failures of components, therefore can isolate a subset for preventative maintenance –Allows testing, repair of online system –Managed by diagnostic processor and network switches via diagnostic network

Slide 27 Hardware techniques (5) Built-in fault injection capabilities –Power control to individual node components –Injectable glitches into I/O and memory busses –Managed by diagnostic processor –Used for proactive hardware introspection »automated detection of flaky components »controlled testing of error-recovery mechanisms –Important for AME benchmarking (see next slide)

Slide 28 “Hardware” techniques (6) Benchmarking –One reason for 1000X processor performance was ability to measure (vs. debate) which is better »e.g., Which most important to improve: clock rate, clocks per instruction, or instructions executed? –Need AME benchmarks “what gets measured gets done” “benchmarks shape a field” “quantification brings rigor”

Slide 29 ISTORE-1 hardware platform 80-node x86-based cluster, 1.4TB storage –cluster nodes are plug-and-play, intelligent, network- attached storage “bricks” »a single field-replaceable unit to simplify maintenance –each node is a full x86 PC w/256MB DRAM, 18GB disk –more CPU than NAS; fewer disks/node than cluster ISTORE Chassis 80 nodes, 8 per tray 2 levels of switches Mbit/s 2 1 Gbit/s Environment Monitoring: UPS, redundant PS, fans, heat and vibration sensors... Intelligent Disk “Brick” Portable PC CPU: Pentium II/266 + DRAM Redundant NICs (4 100 Mb/s links) Diagnostic Processor Disk Half-height canister

Slide 30 A glimpse into the future? System-on-a-chip enables computer, memory, redundant network interfaces without significantly increasing size of disk ISTORE HW in 5-7 years: –building block: 2006 MicroDrive integrated with IRAM »9GB disk, 50 MB/sec from disk »connected via crossbar switch –10,000 nodes fit into one rack! O(10,000) scale is our ultimate design point

Slide 31 Software techniques Fully-distributed, shared-nothing code –centralization breaks as systems scale up O(10000) –avoids single-point-of-failure front ends Redundant data storage –required for high availability, simplifies self-testing –replication at the level of application objects »application can control consistency policy »more opportunity for data placement optimization

Slide 32 Software techniques (2) “River” storage interfaces –NOW Sort experience: performance heterogeneity is the norm »e.g., disks: outer vs. inner track (1.5X), fragmentation »e.g., processors: load (1.5-5x) –So demand-driven delivery of data to apps »via distributed queues and graduated declustering »for apps that can handle unordered data delivery –Automatically adapts to variations in performance of producers and consumers –Also helps with evolutionary growth of cluster

Slide 33 Software techniques (3) Reactive introspection –Use statistical techniques to identify normal behavior and detect deviations from it –Policy-driven automatic adaptation to abnormal behavior once detected »initially, rely on human administrator to specify policy »eventually, system learns to solve problems on its own by experimenting on isolated subsets of the nodes one candidate: reinforcement learning

Slide 34 Software techniques (4) Proactive introspection –Continuous online self-testing of HW and SW »in deployed systems! »goal is to shake out “Heisenbugs” before they’re encountered in normal operation »needs data redundancy, node isolation, fault injection –Techniques: »fault injection: triggering hardware and software error handling paths to verify their integrity/existence »stress testing: push HW/SW to their limits »scrubbing: periodic restoration of potentially “decaying” hardware or software state self-scrubbing data structures (like MVS) ECC scrubbing for disks and memory

Slide 35 Applications ISTORE is not one super-system that demonstrates all these techniques! –Initially provide library to support AME goals Initial application targets –cluster web/ servers »self-scrubbing data structures, online self-testing »statistical identification of normal behavior –decision-support database query execution system »River-based storage, replica management –information retrieval for multimedia data »self-scrubbing data structures, structuring performance-robust distributed computation

Slide 36 Outline 1) Example microprocessor for PostPC gadgets 2) Motivation and the ISTORE project vision –AME: Availability, Maintainability, Evolutionary growth –ISTORE’s research principles –Proposed techniques for achieving AME –Benchmarks for AME Conclusions and future work

Slide 37 Availability benchmark methodology Goal: quantify variation in QoS metrics as events occur that affect system availability Leverage existing performance benchmarks –to generate fair workloads –to measure & trace quality of service metrics Use fault injection to compromise system –hardware faults (disk, memory, network, power) –software faults (corrupt input, driver error returns) –maintenance events (repairs, SW/HW upgrades) Examine single-fault and multi-fault workloads –the availability analogues of performance micro- and macro-benchmarks

Slide 38 Results are most accessible graphically –plot change in QoS metrics over time –compare to “normal” behavior? »99% confidence intervals calculated from no-fault runs Methodology: reporting results Graphs can be distilled into numbers?

Slide 39 Example results: software RAID-5 Test systems: Linux/Apache and Win2000/IIS –SpecWeb ’99 to measure hits/second as QoS metric –fault injection at disks based on empirical fault data »transient, correctable, uncorrectable, & timeout faults 15 single-fault workloads injected per system –only 4 distinct behaviors observed (A) no effect(C) RAID enters degraded mode (B) system hangs(D) RAID enters degraded mode & starts reconstruction –both systems hung (B) on simulated disk hangs –Linux exhibited (D) on all other errors –Windows exhibited (A) on transient errors and (C) on uncorrectable, sticky errors

Slide 40 Example results: multiple-faults Windows reconstructs ~3x faster than Linux Windows reconstruction noticeably affects application performance, while Linux reconstruction does not Windows 2000/IIS Linux/ Apache

Slide 41 Conclusions (1): Benchmarks Linux and Windows take opposite approaches to managing benign and transient faults –Linux is paranoid and stops using a disk on any error –Windows ignores most benign/transient faults –Windows is more robust except when disk is truly failing Linux and Windows have different reconstruction philosophies –Linux uses idle bandwidth for reconstruction –Windows steals app. bandwidth for reconstruction –Windows rebuilds fault-tolerance more quickly Win2k favors fault-tolerance over performance; Linux favors performance over fault-tolerance

Slide 42 Conclusions (2): ISTORE Availability, Maintainability, and Evolutionary growth are key challenges for server systems –more important even than performance ISTORE is investigating ways to bring AME to large-scale, storage-intensive servers –via clusters of network-attached, computationally- enhanced storage nodes running distributed code –via hardware and software introspection –we are currently performing application studies to investigate and compare techniques Availability benchmarks a powerful tool? –revealed undocumented design decisions affecting SW RAID availability on Linux and Windows 2000

Slide 43 Conclusions (3) IRAM attractive for two Post-PC applications because of low power, small size, high memory bandwidth –Gadgets: Embedded/Mobile devices –Infrastructure: Intelligent Storage and Networks PostPC infrastructure requires –New Goals: Availability, Maintainability, Evolution –New Principles: Introspection, Performance Robustness –New Techniques: Isolation/fault insertion, Software scrubbing –New Benchmarks: measure, compare AME metrics

Slide 44 Berkeley Future work IRAM: fab and test chip ISTORE –implement AME-enhancing techniques in a variety of Internet, enterprise, and info retrieval applications –select the best techniques and integrate into a generic runtime system with “AME API” –add maintainability benchmarks »can we quantify administrative work needed to maintain a certain level of availability? –Perhaps look at data security via encryption? –Even consider denial of service?

Slide 45 For more information: The UC Berkeley IRAM/ISTORE Projects: Computers for the PostPC Era

Slide 46 Backup Slides (mostly in the area of benchmarking)

Slide 47 Case study Software RAID-5 plus web server –Linux/Apache vs. Windows 2000/IIS Why software RAID? –well-defined availability guarantees »RAID-5 volume should tolerate a single disk failure »reduced performance (degraded mode) after failure »may automatically rebuild redundancy onto spare disk –simple system –easy to inject storage faults Why web server? –an application with measurable QoS metrics that depend on RAID availability and performance

Slide 48 Benchmark environment: metrics QoS metrics measured –hits per second »roughly tracks response time in our experiments –degree of fault tolerance in storage system Workload generator and data collector –SpecWeb99 web benchmark »simulates realistic high-volume user load »mostly static read-only workload; some dynamic content »modified to run continuously and to measure average hits per second over each 2-minute interval

Slide 49 Benchmark environment: faults Focus on faults in the storage system (disks) How do disks fail? –according to Tertiary Disk project, failures include: »recovered media errors »uncorrectable write failures »hardware errors (e.g., diagnostic failures) »SCSI timeouts »SCSI parity errors –note: no head crashes, no fail-stop failures

Slide 50 Disk fault injection technique To inject reproducible failures, we replaced one disk in the RAID with an emulated disk –a PC that appears as a disk on the SCSI bus –I/O requests processed in software, reflected to local disk –fault injection performed by altering SCSI command processing in the emulation software Types of emulated faults: –media errors (transient, correctable, uncorrectable) –hardware errors (firmware, mechanical) –parity errors –power failures –disk hangs/timeouts

Slide 51 System configuration RAID-5 Volume: 3GB capacity, 1GB used per disk –3 physical disks, 1 emulated disk, 1 emulated spare disk 2 web clients connected via 100Mb switched Ethernet IBM 18 GB 10k RPM Server AMD K MB DRAM Linux or Win2000 IDE system disk = Fast/Wide SCSI bus, 20 MB/sec Adaptec 2940 RAID data disks IBM 18 GB 10k RPM SCSI system disk Disk Emulator AMD K Windows NT 4.0 ASC VirtualSCSI lib. Adaptec 2940 emulator backing disk (NTFS) AdvStor ASC-U2W UltraSCSI Emulated Spare Disk Emulated Disk

Slide 52 Results: single-fault experiments One exp’t for each type of fault (15 total) –only one fault injected per experiment –no human intervention –system allowed to continue until stabilized or crashed Four distinct system behaviors observed (A) no effect: system ignores fault (B) RAID system enters degraded mode (C) RAID system begins reconstruction onto spare disk (D) system failure (hang or crash)

Slide 53 State of the Art: Ultrastar 72ZX –73.4 GB, 3.5 inch disk –2¢/MB –16 MB track buffer –11 platters, 22 surfaces –15,110 cylinders –7 Gbit/sq. in. areal density –17 watts (idle) –0.1 ms controller time –5.3 ms avg. seek (seek 1 track => 0.6 ms) –3 ms = 1/2 rotation –37 to 22 MB/s to media source: 2/14/00 Latency = Queuing Time + Controller time + Seek Time + Rotation Time + Size / Bandwidth per access per byte { + Sector Track Cylinder Head Platter Arm Embed. Proc. Track Buffer