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Lecture 23: Goodbyte to Computer Architecture, Future Predictions, and Your Cal Cultural History : 

Lecture 23: Goodbyte to Computer Architecture, Future Predictions, and Your Cal Cultural History Professor David A. Patterson Computer Science 252 Spring 1998

Final Lecture: 

Final Lecture Review and Goodbye to Computer Architecture, topic by topic + follow-on courses Final Administrivia, include slide total Future Directions for Computer Archtitecture? Learning about your heritage as Cal students/ future alumni Course evaluation by HKN Drinks at LaVal’s

Chapter 1: Performance and Cost: 

Chapter 1: Performance and Cost Amdahl’s Law: CPI Law: Designing to Last through Trends Capacity Speed Logic 2x in 3 years 2x in 3 years DRAM 4x in 3 years 2x in 10 years Disk 4x in 3 years 2x in 5 years Processor 2x every 1.5 years?

Chapter 1: Performance and Cost: 

Chapter 1: Performance and Cost Die Cost goes roughly with die area4 Microprocessor with 100M transistors in 2000? Cost vs. Price Can PC industry support engineering/research investment? (e.g., DEC laying off 15,000) For better or worse, benchmarks shape a field Interested in learning more on integrated circuits? EE 241 “Advanced Digital Integrated Circuits” Interested in learning more on performance? CS 266 “Introduction to Systems Performance”

Goodbye to Performance and Cost: 

Goodbye to Performance and Cost Will sustain 2X every 1.5 years? Can integrated circuits improve below 1.8 micron in speed as well as capacity? 5-6 yrs to PhD => 16X CPU speed, DRAM Capacity, Disk capacity? (1500 MHz CPU, 1GB DRAM, 100 GB disk?)

Chapter 2: Instruction Set Architecture : 

Chapter 2: Instruction Set Architecture What ISA looks like to pipeline? Cray: load/store machine; registers; simple instr. format RISC: Making an ISA that supports pipelined execution 80x86: importance of being their first VLIW/EPIC: compiler controls Instruction Level Parallelism (ILP) Interested in learning more on compilers and ISA? CS 264/5 “Advanced Programming Language Design and Optimization”

Goodbye to Instruction Set Architecture: 

Goodbye to Instruction Set Architecture What did IA-64/EPIC do well besides floating point programs? What happened on EPIC code size vs. x86? Was Intel Oregon increase x86 performance so as to make Intel Santa Clara EPIC performance similar? Did reconfigurable processors (e.g., BRASS, RAW) prove useful? On what class of applications?

Chapters 3/4: Pipelined Implementation: 

Chapters 3/4: Pipelined Implementation Miracle of Pipelining: Bandwidth vs. latency Superscalar breaks single instruction/clock cycle limit Hazards/Dependencies limit: HW & SW techniques to overcome limits Conditional Branches as one Limit: branch prediction Memory system as another limit SW Pipelining: Symbolic Loop Unrolling to get most from pipeline with little code expansion, little overhead Scoreboard: Allow instructions behind stall to proceed Out-of-order execution: Helps cache misses as well Reservations stations: renaming to larger set of registers + buffering source operands Prevents registers as bottleneck Avoids WAR, WAW hazards of Scoreboard Beyond basic block

Goodbye to Pipelined Implementation: 

Goodbye to Pipelined Implementation Did wider superscalar, more out-of-order machines work well, or were they beyond the point of diminishing returns? What about more exotic ideas? Value prediction: predict the next value of a variable (e.g., loop counter) to get by dependencies? Simulatenous Multithreading: since getting little benefit from most programs of wide superscalar, out-of-order machines, schedule multiple threads to get most of hardware

Appendix B: Vector Processors: 

Appendix B: Vector Processors Vector is alternative model for exploiting ILP Accomodates long memory latency, doesn’t rely on caches as does Out-Of-Order, superscalar/VLIW designs If code is vectorizable, then simpler hardware, more energy efficient, and better real-time model than Out-of-order machines Design issues include number of lanes, number of functional units, number of vector registers, length of vector registers, exception handling, conditional operations What % of computation is vectorizable? What % do compilers deliver? For new apps?

DSP architectures: 

DSP architectures Continuous I/O stream, real time requirements Multiple memory accesses Datapath: Multiply width, Wide accumulator, Guard bits/shiting rounding, Saturation Autoinc/autodec addressing Weird things: Circular & Reverse addressing Special instructions shift left and saturate (arithmetic left-shift) zero overhead loops

Goodbye to Vectors, DSPs: 

Goodbye to Vectors, DSPs Multimedia instructions (Intel MMX, HP MMX, SPARC VIS, Motorola AltiVec) represent a resurgence of vector-like instructions: where they hype, or did they really help performance of multimedia apps? Did vector prove to be a better match to new apps such as multidemia & DSP, programming in HLL? Did DSPs survive distinct from microprocessors?

Chapter 5: Memory Hierarchy: 

Chapter 5: Memory Hierarchy Processor-DRAM Performance gap 1/3 to 2/3 die area for caches, TLB Alpha 21264: 108 clock to memory Þ 648 instruction issues during miss 3 Cs: Compulsory, Capacity, Conflict 4 Questions: where, who, which, write Applied recursively to create multilevel caches Performance = f(hit time, miss rate, miss penalty) danger of concentrating on just one when evaluating performance Integration of Processors into Memory, into Disks? CS 294-2 (Patterson) Fall 1998, Control No.: 25160 MPU 60%/yr. DRAM 7%/yr.

Cache Optimization Summary: 

Cache Optimization Summary Technique MR MP HT Complexity Larger Block Size + – 0 Higher Associativity + – 1 Victim Caches + 2 Pseudo-Associative Caches + 2 HW Prefetching of Instr/Data + 2 Compiler Controlled Prefetching + 3 Compiler Reduce Misses + 0 Priority to Read Misses + 1 Subblock Placement + + 1 Early Restart & Critical Word 1st + 2 Non-Blocking Caches + 3 Second Level Caches + 2 Small & Simple Caches – + 0 Avoiding Address Translation + 2 Pipelining Writes + 1 miss rate hit time miss penalty memory hierarchy art: taste in selecting between alternatives to find combination that fits well together

Goodbye to Memory Hierarchy: 

Goodbye to Memory Hierarchy Will L2 cache keep growing? (e.g, 64 MB L2 cache?) Will multilevel hierarchy get deeper? (e.g, l4 cache?) Will DRAM capacity/chip keep going at 4X / 3 years? (e.g., 16 Gbit chip?) Will processor and DRAM/Disk be unified? For which apps? Out-of-order CPU hides L1 data cache miss (3–5 clocks), but hide L2 miss? (>100 clocks) Memory hierarchy likely overriding issue in algorithm performance: do algorithms and data structures of 1960s work with machines of 2000s?

CS 252 Administrivia: 

CS 252 Administrivia Projects by 5PM Mon. May 11; send email when done Many, many interesting projects Several students and faculty said they enjoyed poster session and mentioned what great jobs you did Former CS252 TA, now a professor, remarked at how much better You have seen the full conference cycle: topic selection, investigation, real deadlines, oral presentation, poster session, written presentation Many capable of being turned into published papers Hope you noticed that feedback was important for both your ideas and your presentation of ideas, and the benefit of presenting preliminary results before the final deadline 252 lectures slides on line: 1062 more slides than pages of textbook!

Chapter 6: Storage I/O: 

Chapter 6: Storage I/O Disk BW 40%/yr, areal density 60%/ yr, $/MB faster? Little’s Law: Lengthsystem = rate x Timesystem (Mean number customers = arrival rate x mean service time) Througput vs. response time Value of faster response time on productivity Benchmarks: scaling, cost, auditing, response time limits RAID: performance and reliability Queueing theory? IEOR 161, 267, 268 SW storage systems? CS 286 “Implementation of Data Base Systems” 1 3 5

Goodbye to Storage I/O: 

Goodbye to Storage I/O Disks growing at 4X/ 3 years more recently: Will I continue get email messages to reduce file storage for the rest of my career? Heading towards a personal terabyte: hierarchical file systems vs. database to organize personal storage? Disks attached directly to networks, avoiding the file server? (“Network Attached Storage Devices”) What going to do when can have video record of entire life on line?

Chapter 7: Networks: 

Chapter 7: Networks Sender Receiver Transmission time (size ÷ bandwidth) Time of Flight Receiver Overhead Transport Latency Total Latency = Sender Overhead + Time of Flight + Message Size ÷ BW + Receiver Overhead Total Latency (processor busy) (processor busy) High BW networks + high overheads violate of Amdahl’s Law

Chapter 7: Networks: 

Chapter 7: Networks Similarities of MPP interconnects, LANs, WANs Integrated circuit revolutionizing networks as well as processors Switch is a specialized computer Protocols allow hetereogeneous networking , handle normal and abnormal events Interested in learning more on networks? EE 122 “Introduction to Computer Networks” (McCanne) CS 268 “Computer Networks” (McCanne)

Goodbye to Networks: 

Goodbye to Networks Will network interfaces follow example of graphics interfaces and become first class citizens in microprocessors, thereby avoiding the I/O bus? Will Ethernet standard keep winning the LAN wars? e.g., 100 Mbit/sec, 1 Gbit/sec, 10 Gbit/sec, ... ? Who will win the WAN wars long term: telephony vs TCP/IP bigots?

Chapter 8: Multiprocessors: 

Shared, uniform memory access vs. Shared non-uniform memory access vs. Message Passing Cache coherency protocols: Snooping vs. directory Interested in learning more on multiprocessors: CS 258 “Parallel Computer Architecture” (Spring 99) E 267 “Programming Parallel Computers” Chapter 8: Multiprocessors

Goodbye to Multiprocessors: 

Goodbye to Multiprocessors Successful today for file servers, time sharing, databases, graphics; will parallel programming become standard for production programs? If so, what enabled it: new programming languauges, new data structures, new hardware, new coures, ...? Which won large scale number crunching, databases: Clusters of independent computers connected via switched LAN vs. large shared NUMA machines? Why?

How to be a Success in Graduate School: 

How to be a Success in Graduate School 1) “Swim or Sink” “Success is determined by me (student) primarily” Faculty will set up the opportunity, but its up to me leverage it 2) “Read/learn on your own” “Related to 1), I think you told me this as you handed me a stack of about 20 papers” 3) “Teach your advisor” “I really liked this concept; go out and learn about something and then teach the professor” Fast moving field, don’t expect prof to be at forefront everywhere

Role Changes during Project: 

Role Changes during Project

Alternatives to a Bad Career: 

Alternatives to a Bad Career Goal is to have impact: Change way people do Computer Science & Engineering Evaluation of academic research uses bad benchmarks => skews academic behavior Many 3 - 5 year projects gives more chances for impact Feedback is key: seek out & value critics Do “Real Stuff”: make sure you are solving some problem that someone cares about Taste is critical in selecting research problems, solutions, experiments, & communicating results; taste is acquired and improved by feedback Students are the coin of the academic realm

Future Directions in Computer Architecture: 

1000X performance increase in “stationary” computers, consolidation of industry => time for architecture/OS/compiler researchers declare victory, search for new horizons? Apps/metrics of future to design computer of future! Mobile Multimedia (PDA, wearable) offer many new challenges: energy efficiency, size, real time performance PDA of future + VIRAM-1 one example, hope others will follow 3D Telepresense: being there digitally (and virtually) will be as good as being there physically “be there" at the opening ceremonies for the next Olympics: parade around the track with the Olympians and join the final torch runner for her dash up the stairs to light the Olympic torch! Future Directions in Computer Architecture

Cal Cultural History: ABCs of American Football: 

Cal Cultural History: ABCs of American Football Cal Cultural History: ABCs of American Football Started with “soccer”; still 11 on a team, 2 teams, 1 ball, on a field; object is to move ball into “goal”; most goals wins New World changes the rules to increase scoring: Make goal bigger! (full width of field) Carry ball with hands Can toss ball to another player backwards or laterally (called a “lateral”) anytime & forwards (“pass”) sometimes How to stop players carrying the ball? Grab them & knock them down by making knee hit the ground (“tackle”) if drop ball (“fumble”), other players can pick it up and score Score by moving ball into goal (“cross the goal line” or “into the end zone”) scoring a “touchdown” (6 points), or kicking ball between 2 poles (“goal posts”) scoring a “field goal” (3, unless after touchdown = 1: “extra point” ) Kick ball to other team after score (“kickoff”); laterals OK Game ends when no time left (4 15 min quarters) & person with ball is stopped (Soccer time only: 2 45 min halves, time stops play)

Football Field: 

Football Field 50 40 30 20 10 40 30 20 10 Goal Line End Zone End Zone Goal Line 100 yards (91.4 meters) Califorina Golden Bears Cal

The Spectacle of Football: 

The Spectacle of Football Rose Bowl: Prestigious bonus game played January 1 if have a great year (“playoffs”) preceeded by parade national TV coverage 1929 Rose Bowl Game Cal vs. Georgia Tech Cal going left to right (==>), GeorgiaTech right to left (<==) Georgia Tech player fumbles football Cal player, Roy Reigel, picks up football and tries to avoid Georgia Tech players Let’s see what happens on video The Spectacle of Football

The Spectacle of Football: 

The Spectacle of Football Play nearby archrival for last game of season Cal’s archrival is Stanford; stereotype is Private, Elitist, Snobs The Big Game: Cal vs. Stanford, winner gets a trophy (“The Axe”) : Oldest rivalry west of Mississippi; 100th in 1997 American college football is a spectacle School colors (Cal: Blue & Gold; Stanford: Red & White) School nicknames (Cal: Golden Bear; Stanford: Cardinal) School mascot (Cal: Oski the bear; Stanford: a tree(!)) Leaders of cheers (“cheerleaders”) “Bands” (orchestras that march) from both schools at games; before game, at halftime, after game Stanford Band more like a drinking club; ­ “Animal House” Plays one song: “All Right Now” Stanford used to yell “boring” at band during Cal’s performance

1982 Big Game: 

1982 Big Game “There has never been anything in the history of college football to equal it for sheer madness.” Sports Illustrated Cal coach is Joe Kapp, former Cal player; tells team to play 100% for 60 minutes (“40 for 60”; “Bear will not die”); 1st year as coach; lasts 5 years (“Never give up”) Stanford coach is Paul Wiggin, former Stanford player, lots of coaching experience; fired from job next year Stanford Quarterback is John Elway, who goes on to be a professional All Star football player (still playing today, won 1st Superbowl in 1998) Cal Quarterback is Gail Gilbert, who goes on to be a non-starting professional football player (stoped playing 1996) Stanford lost 4 games in last few minutes of game Let’s see what happens on video

Notes About “The Play”: 

Notes About “The Play” Cal only had 10 men on the field; last second another came on (170 pound Steve Dunn #3) & makes key 1st block Kevin Moen #26: 6’1” 190 lb. safety, never scored in 4 years at Cal laterals to Rodgers (and doesn’t give up) Richard Rodgers #5: 6’ 200 lb. safety, “Don’t fall with the ball.” laterals to Garner Dwight Garner #43: 5’9” 185 pound running back almost tackled, 2 legs & 1 arm pinned, laterals to Rodgers Richard Rodgers #5 (again): “Give me the ball, Dwight.” laterals to Ford Mariet Ford #1: 5’9”, 165 pound wide receiver leg cramps, overhead blind lateral to Moen & blocks 3 players Moen (again) cuts through Stanford band into end zone On field for Stanford: 22 football players, 3 Axe committee members, 3 cheerleaders, 144 Stanford band members (172 for Stanford v. 11 for Cal) “Weakest part of the Stanford defense was the woodwinds.” 4 Cal players + Stanford Trombonist (Gary Tyrrell) hold reunion every year at Big Game; Stanford revises history (20-19 on Axe)

Your Cal Cultural History: 

Your Cal Cultural History Cal students/alumni heritage is the greatest college football play in > 100 years Cal students/alumni work hard and play hard Cal students/alumni handle adversity Cal students/alumni never give up! Cal students/alumni triumph over great odds!

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