Presentation on theme: "CS136, Advanced Architecture Limits to ILP Simultaneous Multithreading."— Presentation transcript:
CS136, Advanced Architecture Limits to ILP Simultaneous Multithreading
CS136 2 Outline Limits to ILP (another perspective) Thread Level Parallelism Multithreading Simultaneous Multithreading Power 4 vs. Power 5 Head to Head: VLIW vs. Superscalar vs. SMT Commentary Conclusion
CS136 3 Limits to ILP Conflicting studies of amount –Benchmarks (vectorized Fortran FP vs. integer C programs) –Hardware sophistication –Compiler sophistication How much ILP is available using existing mechanisms with increasing HW budgets? Do we need to invent new HW/SW mechanisms to keep on processor performance curve? –Intel MMX, SSE (Streaming SIMD Extensions): 64 bit ints –Intel SSE2: 128 bit, including 2 64-bit Fl. Pt. per clock –Motorola AltiVec: 128 bit ints and FPs –Supersparc Multimedia ops, etc.
CS136 4 Overcoming Limits Advances in compiler technology + significantly new and different hardware techniques may be able to overcome limitations assumed in studies However, unlikely such advances when coupled with realistic hardware will overcome these limits in near future
CS136 5 Limits to ILP Initial HW Model here; MIPS compilers. Assumptions for ideal/perfect machine to start: 1. Register renaming – infinite virtual registers => all register WAW & WAR hazards are avoided 2. Branch prediction – perfect; no mispredictions 3. Jump prediction – all jumps perfectly predicted (returns, case statements) 2 & 3 no control dependencies; perfect speculation & an unbounded buffer of instructions available 4. Memory-address alias analysis – addresses known & a load can be moved before a store provided addresses not equal; 1&4 eliminates all but RAW Also: perfect caches; 1 cycle latency for all instructions (FP *,/); unlimited instructions issued/clock cycle
CS136 6 ModelPower 5 Instructions Issued per clock Infinite4 Instruction Window Size Infinite200 Renaming Registers Infinite48 integer + 40 Fl. Pt. Branch PredictionPerfect2% to 6% misprediction (Tournament Branch Predictor) CachePerfect64KI, 32KD, 1.92MB L2, 36 MB L3 Memory Alias Analysis Perfect?? Limits to ILP HW Model comparison
CS136 7 Upper Limit to ILP: Ideal Machine (Figure 3.1) Integer: FP: Instructions Per Clock
CS136 8 New ModelModelPower 5 Instructions Issued per clock Infinite 4 Instruction Window Size Infinite, 2K, 512, 128, 32 Infinite200 Renaming Registers Infinite 48 integer + 40 Fl. Pt. Branch Prediction Perfect 2% to 6% misprediction (Tournament Branch Predictor) CachePerfect 64KI, 32KD, 1.92MB L2, 36 MB L3 Memory Alias Perfect ?? Limits to ILP HW Model comparison
CS New ModelModelPower 5 Instructions Issued per clock 64Infinite4 Instruction Window Size 2048Infinite200 Renaming Registers Infinite 48 integer + 40 Fl. Pt. Branch Prediction Perfect vs. 8K Tournament vs bit vs. profile vs. none Perfect2% to 6% misprediction (Tournament Branch Predictor) CachePerfect 64KI, 32KD, 1.92MB L2, 36 MB L3 Memory Alias Perfect ?? Limits to ILP HW Model comparison
CS More Realistic HW: Branch Impact Figure 3.3 Change from Infinite window to 2048, and maximum issue of 64 instructions per clock cycle ProfileBHT (512)TournamentPerfect No prediction FP: Integer: IPC
CS Misprediction Rates
CS New ModelModelPower 5 Instructions Issued per clock 64Infinite4 Instruction Window Size 2048Infinite200 Renaming Registers Infinite v. 256, 128, 64, 32, none Infinite48 integer + 40 Fl. Pt. Branch Prediction 8K 2-bitPerfectTournament Branch Predictor CachePerfect 64KI, 32KD, 1.92MB L2, 36 MB L3 Memory Alias Perfect Limits to ILP HW Model comparison
CS More Realistic HW: Renaming Register Impact (N int + N fp) Figure 3.5 Change to 2048 instr window, 64 instr issue, 8K 2 level Prediction 64 None256Infinite32128 Integer: FP: IPC
CS New ModelModelPower 5 Instructions Issued per clock 64Infinite4 Instruction Window Size 2048Infinite200 Renaming Registers 256 Int FPInfinite48 integer + 40 Fl. Pt. Branch Prediction 8K 2-bitPerfectTournament CachePerfect 64KI, 32KD, 1.92MB L2, 36 MB L3 Memory Alias Perfect v. Stack v. Inspect v. none Perfect Limits to ILP HW Model comparison
CS New ModelModelPower 5 Instructions Issued per clock 64 (no restrictions) Infinite4 Instruction Window Size Infinite vs. 256, 128, 64, 32 Infinite200 Renaming Registers 64 Int + 64 FPInfinite48 integer + 40 Fl. Pt. Branch Prediction 1K 2-bitPerfectTournament CachePerfect 64KI, 32KD, 1.92MB L2, 36 MB L3 Memory Alias HW disambiguation Perfect Limits to ILP HW Model comparison
CS Realistic HW: Window Impact (Figure 3.7) Perfect disambiguation (HW), 1K Selective Prediction, 16 entry return, 64 registers, issue as many as window Infinite Integer: FP: IPC
CS Outline Limits to ILP (another perspective) Thread Level Parallelism Multithreading Simultaneous Multithreading Power 4 vs. Power 5 Head to Head: VLIW vs. Superscalar vs. SMT Commentary Conclusion
CS How to Exceed ILP Limits of This Study? These are not laws of physics –Just practical limits for today –Could be overcome via research Compiler and ISA advances could change results WAR and WAW hazards through memory: eliminated WAW and WAR hazards through register renaming, but not in memory usage –Can get conflicts via allocation of stack frames –Because called procedure reuses memory addresses of previous stack frames
CS HW v. SW to increase ILP Memory disambiguation: HW best Speculation: –HW best when dynamic branch prediction better than compile-time prediction –Exceptions easier for HW –HW doesn’t need bookkeeping code or compensation code –Very complicated to get right in SW Scheduling: SW can look ahead to schedule better Compiler independence: HW does not require new compiler to run well
CS Performance Beyond Single-Thread ILP Much higher natural parallelism in some applications –Database or scientific codes Explicit thread-level or data-level parallelism Thread: has own instructions and data –May be part of parallel program or independent program –Each thread has all state (instructions, data, PC, register state, and so on) needed to execute Data-level parallelism: Perform identical operations on lots of data
CS Thread Level Parallelism (TLP) ILP exploits implicit parallel operations within loop or straight-line code segment TLP explicitly represented by multiple threads of execution that are inherently parallel Goal: Use multiple instruction streams to improve –Throughput of computers that run many programs –Execution time of multi-threaded programs TLP could be more cost-effective to exploit than ILP
CS Do Both ILP and TLP? TLP and ILP exploit two different kinds of parallel structure in a program Could a processor oriented to ILP still exploit TLP? –Functional units are often idle in data path designed for ILP because of either stalls or dependencies in the code Could TLP be used as source of independent instructions that might keep the processor busy during stalls? Could TLP be used to employ functional units that would otherwise lie idle when insufficient ILP exists?
CS New Approach: Multithreaded Execution Multithreading: multiple threads share functional units of 1 processor via overlapping –Processor must duplicate independent state of each thread »Separate copy of register file, PC »Separate page table if different process –Memory sharing via virtual memory mechanisms »Already supports multiple processes –HW for fast thread switch »Must be much faster than full process switch (which is 100s to 1000s of clocks) When to switch? –Alternate instruction per thread (fine grain)—round robin –When thread is stalled (coarse grain) »E.g., cache miss
CS Fine-Grained Multithreading Switches between threads on each instruction, interleaving execution of multiple threads Usually done round-robin, skipping stalled threads CPU must be able to switch threads every clock Advantage: can hide both short and long stalls –Instructions from other threads always available to execute –Easy to insert on short stalls Disadvantage: slows individual threads –Thread ready to execute without stalls will be delayed by instructions from other threads Used on Sun’s Niagara (will see later)
CS Course-Grained Multithreading Switches threads only on costly stalls –E.g., L2 cache misses Advantages –Relieves need to have very fast thread switching –Doesn’t slow thread »Other threads only issue instructions when main one would stall (for long time) anyway Disadvantage: pipeline startup costs make it hard to hide throughput losses from shorter stalls –Pipeline must be emptied or frozen on stall, since CPU issues instructions from only one thread –New thread must fill pipe before instructions can complete –Thus, better for reducing penalty of high-cost stalls where pipeline refill << stall time Used in IBM AS/400
CS Simultaneous Multithreading (SMT) Simultaneous multithreading (SMT): insight that dynamically scheduled processor already has many HW mechanisms to support multithreading –Large set of virtual registers that can be used to hold register sets for independent threads –Register renaming provides unique register identifiers »Instructions from multiple threads can be mixed in data path »Without confusing sources and destinations across threads! –Out-of-order completion allows the threads to execute out of order, and get better utilization of the HW Just add per-thread renaming table and keep separate PCs –Independent commitment can be supported via separate reorder buffer for each thread Source: Micrprocessor Report, December 6, 1999 “Compaq Chooses SMT for Alpha”
CS Simultaneous Multithreading MMFX FP BRCC Cycle One thread, 8 units M = Load/Store, FX = Fixed Point, FP = Floating Point, BR = Branch, CC = Condition Codes MMFX FP BRCC Cycle Two threads, 8 units
CS Design Challenges in SMT What is impact on single thread performance? –Preferred-thread approach »Sacrifices neither throughput nor single-thread performance? »Nope: processor will sacrifice some throughput when preferred thread stalls Larger register file needed to hold multiple contexts Must not affect clock cycle, especially in: –Instruction issue—more candidate instructions to consider –Instruction completion—hard to choose which to commit Must ensure that cache and TLB conflicts caused by SMT don’t degrade performance
CS Digression: Covert Channels Imagine you’re spy with account on Knuth –Want to communicate a secret to Geoff –Secret is reasonably small –FBI is watching your account and your Solution: process spawning –Once a second, Geoff spawns process »Records own PID, waits 10 ms, forks & records child PID –Once a second, you send one bit of information »If bit is zero, you do nothing »If bit is one, you spawn processes as fast as possible –If Geoff sees big PID gap, he records “1”, else “0” Many variations on this basic idea
CS Covert-Channel Attacks on Crypto Most (not all) crypto code behaves differently on “1” bit in key vs. “0” bit –Runs longer or shorter –Uses more or less power –Accesses different memory –Etc. Usually called “information leakage” Has been successfully used in lab to crack strong crypto –Even recovering some bits makes brute-force attack practical for getting remainder –Some modern implementations try to fight by doing wasted work on shorter path of “if”, etc.
CS SMT Attack on SSH On SMT machine, lower-priority thread’s execution rate depends on higher-priority one’s instructions –More stalls in top thread mean more speed in bottom one –Stalls vary depending on what crypto code is doing »Operates at very low level »Thus much harder to defend against Successful attack on ssh keys has been demonstrated in lab Best known defense: don’t do SMT –Careful coding of crypto could probably also work –Note that this also applies to things like cache and TLB –Lots of ways to leak information unintentionally!
CS Power 4 Single-threaded predecessor to Power 5. 8 execution units in out-of-order engine; each can issue instruction each cycle.
CS Power 4 Power 5 2 fetch (PC), 2 initial decodes 2 commits (architected register sets)
CS Power 5 data flow... Why only 2 threads? With 4, one of the shared resources (physical registers, cache, memory bandwidth) would be prone to bottleneck
CS Power 5 thread performance... Relative priority of each thread controllable in hardware. For balanced operation, both threads run slower than if they “owned” the machine.
CS Changes in Power 5 to support SMT Increased associativity of L1 instruction cache and instruction address translation buffers Added per-thread load and store queues Increased size of L2 (1.92 vs MB) and L3 caches Added separate instruction prefetch and buffering per thread Increased virtual registers from 152 to 240 Increased size of several issue queues Power5 core is about 24% larger than Power4 because of SMT support
CS Initial Performance of SMT Pentium 4 Extreme SMT yields 1.01 speedup for SPECint_rate benchmark; 1.07 for SPECfp_rate –Pentium 4 is dual-threaded SMT –SPECRate requires each benchmark to be run against vendor- selected number of copies of same benchmark Pairing each of 26 SPEC benchmarks with every other on Pentium 4 (26 2 runs) gives speedups from 0.90 to 1.58; average was processor Power 5 server 1.23 faster for SPECint_rate w/ SMT, 1.16 faster for SPECfp_rate Power 5 running 2 copies of each app had speedup between 0.89 and 1.41 –Most gained some –Floating-point apps had most cache conflicts and least gains
CS Head-to-Head ILP Competition ProcessorMicro architectureFetch / Issue / Execute FUClock Rate (GHz) Transis -tors Die size Power Intel Pentium 4 Extreme Speculative dynamically scheduled; deeply pipelined; SMT 3/3/47 int. 1 FP M 122 mm W AMD Athlon 64 FX-57 Speculative dynamically scheduled 3/3/46 int. 3 FP M 115 mm W IBM Power5 (1 CPU only) Speculative dynamically scheduled; SMT; 2 CPU cores/chip 8/4/86 int. 2 FP M 300 mm 2 (est.) 80W (est.) Intel Itanium 2 Statically scheduled VLIW-style 6/5/119 int. 2 FP M 423 mm W
CS No Silver Bullet for ILP No obvious overall leader in performance AMD Athlon leads on SPECInt performance, followed by the Pentium 4, Itanium 2, and Power5 Itanium 2 and Power5 clearly dominate Athlon and Pentium 4 on SPECFP Itanium 2 is most inefficient processor both for floating-point and integer code for all but one efficiency measure (SPECFP/Watt) Athlon and Pentium 4 both use transistors and area efficiently IBM Power5 is most effective user of energy on SPECFP, essentially tied on SPECINT
CS Limits to ILP Doubling issue rates above today’s 3-6 instructions per clock probably requires processor to: –Issue 3-4 data-memory accesses per cycle, –Resolve 2-3 branches per cycle, –Rename and access over 20 registers per cycle, and –Fetch instructions per cycle. Complexity of implementing these capabilities is likely to mean sacrifices in maximum clock rate –E.g, widest-issue processor is Itanium 2 –It also has slowest clock rate –Despite consuming the most power!
CS Limits to ILP (cont’d) Most ways to increase performance also boost power consumption Key question is energy efficiency: does a method increase power consumption faster than it boosts performance? Multiple-issue techniques are energy inefficient: –Incurs logic overhead that grows faster than issue rate –Growing gap between peak issue rates and sustained performance Number of transistors switching = f(peak issue rate); performance = f(sustained rate); growing gap between peak and sustained performance Increasing energy per unit of performance
CS Commentary Itanium is not significant breakthrough in scaling ILP or in avoiding problems of complexity and power consumption Instead of pursuing more ILP, architects turning to TLP using single-chip multiprocessors In 2000, IBM announced Power4, 1st commercial single-chip, general-purpose multiprocessor: has two Power3 processors and integrated L2 cache –Sun Microsystems, AMD, and Intel have also switched focus from aggressive uniprocessors to single-chip multiprocessors Right balance of ILP and TLP is unclear today –Maybe desktops (mostly single-threaded?) need different design than servers (can do lots of TLP)
CS And in conclusion … Limits to ILP (power efficiency, compilers, dependencies …) seem to limit to 3 to 6 issue for practical options Explicitly parallel (Data level parallelism or Thread level parallelism) is next step to performance Coarse grain vs. Fine grained multihreading –Only on big stall vs. every clock cycle Simultaneous Multithreading if fine grained multithreading based on OOO superscalar microarchitecture –Instead of replicating registers, reuse rename registers Itanium/EPIC/VLIW is not a breakthrough in ILP Balance of ILP and TLP decided in marketplace