Processor Pipeline

Total Page:16

File Type:pdf, Size:1020Kb

Processor Pipeline Spring 2016 :: CSE 502 – Computer Architecture Processor Pipeline Nima Honarmand Spring 2016 :: CSE 502 – Computer Architecture Generic Instruction Cycle • Steps in processing an instruction: – Instruction Fetch (IF_STEP) – Instruction Decode (ID_STEP) – Operand Fetch (OF_STEP) • Might be from registers or memory – Execute (EX_STEP) • Perform computation on the operands – Result Store or Write Back (RS_STEP) • Write the execution results back to registers or memory • ISA determines what needs to be done in each step for each instruction • μArch determines how HW implements the steps Spring 2016 :: CSE 502 – Computer Architecture Datapath vs. Control Logic • Datapath is the collection of HW components and their connection in a processor – Determines the static structure of processor • Control logic determines the dynamic flow of data between the components – E.g., the control lines of MUXes and ALU in last slide – Is a function of? • Instruction words • State of the processor • Execution results at each stage Spring 2016 :: CSE 502 – Computer Architecture Generic Datapath Components • Main components – Instruction Cache – Data Cache – Register File – Functional Units (ALU, Floating Point Unit, Memory Unit, …) – Pipeline Registers • Auxiliary Components (in advanced processors) – Reservation Stations – Reorder Buffer – Branch Predictor – Prefetchers – … • Lots of glue logic (often multiplexors) to glue these together Spring 2016 :: CSE 502 – Computer Architecture Example: MIPS Instruction Set • All instructions are 32 bits Spring 2016 :: CSE 502 – Computer Architecture A Simple MIPS Datapath Write-Back (WB) +1 PC Reg ALU File I-cache D-cache Inst. Decode & Inst. Fetch Register Read Execute Memory (IF) (ID) (EX) (MEM) IF_STEP ID_STEP OF_STEP EX_STEP RS_STEP Spring 2016 :: CSE 502 – Computer Architecture Single-Instruction Datapath Single-cycle ins0.(fetch,dec,ex,mem,wb) ins1.(fetch,dec,ex,mem,wb) Multi-cycle ins0.fetch ins0.(dec,ex) ins0.(mem,wb) ins1.fetch ins1.(dec,ex) ins1.(mem,wb) time • Process one instruction at a time • Single-cycle control: hardwired – Low CPI (1) – Long clock period (to accommodate slowest instruction) • Multi-cycle control: typically micro-programmed – Short clock period – High CPI • Can we have both low CPI and short clock period? – Not if datapath executes only one instruction at a time – No good way to make a single instruction go faster Spring 2016 :: CSE 502 – Computer Architecture Pipelined Datapath Multi-cycle ins0.fetch ins0.(dec,ex) ins0.(mem,wb) ins1.fetch ins1.(dec,ex) ins1.(mem,wb) ins0.fetch ins0.(dec,ex) ins0.(mem,wb) Pipelined ins1.fetch ins1.(dec,ex) ins1.(mem,wb) time ins2.fetch ins2.(dec,ex) ins2.(mem,wb) • Start with multi-cycle design • When insn0 goes from stage 1 to stage 2 … insn1 starts stage 1 • Each instruction passes through all stages … but instructions enter and leave at faster rate Style Ideal CPI Cycle Time (1/freq) Single-cycle 1 Long Multi-cycle > 1 Short Pipelined 1 Short Pipeline can have as many insns in flight as there are stages Spring 2016 :: CSE 502 – Computer Architecture Pipeline Illustrated Comb. Logic L n Gate Delay BW = ~(1/n) n n L -- Gate L -- Gate 2 Delay 2 Delay BW = ~(2/n) n n n Gate L -- Gate L -- Gate L -- 3Delay 3Delay 3 Delay BW = ~(3/n) Pipeline Latency = n Gate Delay + (p-1) register delays p: # of stages Improves throughput at the expense of latency Spring 2016 :: CSE 502 – Computer Architecture 5-Stage MIPS Pipeline Spring 2016 :: CSE 502 – Computer Architecture Stage 1: Fetch • Fetch an instruction from instruction cache every cycle – Use PC to index instruction cache – Increment PC (assume no branches for now) • Write state to the pipeline register (IF/ID) – The next stage will read this pipeline register Spring 2016 :: CSE 502 – Computer Architecture Stage 1: Fetch Diagram target M U X 1 + PC + 1 + PC Decode PC en Instruction Cache bits Instruction en IF / ID Pipeline register Spring 2016 :: CSE 502 – Computer Architecture Stage 2: Decode • Decodes opcode bits – Set up Control signals for later stages • Read input operands from register file – Specified by decoded instruction bits • Write state to the pipeline register (ID/EX) – Opcode – Register contents, immediate operand – PC+1 (even though decode didn’t use it) – Control signals (from insn) for opcode and destReg Spring 2016 :: CSE 502 – Computer Architecture Stage 2: Decode Diagram target PC +1 PC regA regB PC + 1 + PC regA contents Register File Fetch destReg Execute data regB contents en bits imm Instruction Control Signals/ IF / ID ID / EX Pipeline register Pipeline register Spring 2016 :: CSE 502 – Computer Architecture Stage 3: Execute • Perform ALU operations – Calculate result of instruction • Control signals select operation • Contents of regA used as one input • Either regB or constant offset (imm from insn) used as second input – Calculate PC-relative branch target • PC+1+(constant offset) • Write state to the pipeline register (EX/Mem) – ALU result, contents of regB, and PC+1+offset – Control signals (from insn) for opcode and destReg Spring 2016 :: CSE 502 – Computer Architecture Stage 3: Execute Diagram target PC+1 + +offset PC + 1 + PC A ALU regA L result contents M U Decode U Memory regB X regB contents contents imm Control Signals destReg Control Signals/ data ID / EX EX/Mem Pipeline register Pipeline register Spring 2016 :: CSE 502 – Computer Architecture Stage 4: Memory • Perform data cache access – ALU result contains address for LD or ST – Opcode bits control R/W and enable signals • Write state to the pipeline register (Mem/WB) – ALU result and Loaded data – Control signals (from insn) for opcode and destReg Spring 2016 :: CSE 502 – Computer Architecture Stage 4: Memory Diagram target PC+1 +offset ALU result ALU in_addr result back - in_data Execute Write Data Cache data Loaded regB contents en R/W signals signals Control Control destReg data EX/Mem Mem/WB Pipeline register Pipeline register Spring 2016 :: CSE 502 – Computer Architecture Stage 5: Write-back • Writing result to register file (if required) – Write Loaded data to destReg for LD – Write ALU result to destReg for ALU insn – Opcode bits control register write enable signal Spring 2016 :: CSE 502 – Computer Architecture Stage 5: Write-back Diagram ALU result data Loaded M Memory data U X signals Control M destReg U Mem/WB X Pipeline register Spring 2016 :: CSE 502 – Computer Architecture Putting It All Together M U X 1 + target + PC+1 PC+1 instruction eq? ALU regA M regB result Register valA A U Inst ALU X PC File L mdata Cache data result valB M Data dest U U Cache X data dest valB imm M U X Control signals/ signals signals Control Control IF/ID ID/EX EX/Mem Mem/WB Spring 2016 :: CSE 502 – Computer Architecture Issues With Pipelining Spring 2016 :: CSE 502 – Computer Architecture Pipelining Idealism • Uniform Sub-operations – Operation can partitioned into uniform-latency sub-ops • Repetition of Identical Operations – Same ops performed on many different inputs • Independent Operations – All ops are mutually independent Spring 2016 :: CSE 502 – Computer Architecture Pipeline Realism • Uniform Sub-operations … NOT! – Balance pipeline stages • Stage quantization to yield balanced stages • Minimize internal fragmentation (left-over time near end of cycle) • Repetition of Identical Operations … NOT! – Unifying instruction types • Coalescing instruction types into one “multi-function” pipe • Minimize external fragmentation (idle stages to match length) • Independent Operations … NOT! – Resolve data and resource hazards • Inter-instruction dependency detection and resolution Pipelining is expensive Spring 2016 :: CSE 502 – Computer Architecture The Generic Instruction Pipeline Instruction Fetch IF Instruction Decode ID Operand Fetch OF Instruction Execute EX Write-back WB Spring 2016 :: CSE 502 – Computer Architecture Balancing Pipeline Stages IF TIF= 6 units Without pipelining ID Tcyc TIF+TID+TOF+TEX+TOS TID= 2 units = 31 Pipelined OF TID= 9 units Tcyc max{TIF, TID, TOF, TEX, TOS} = 9 EX Speedup = 31 / 9 = 3.44 TEX= 5 units WB TOS= 9 units Can we do better? Spring 2016 :: CSE 502 – Computer Architecture Balancing Pipeline Stages (1/2) • Two methods for stage quantization – Divide sub-ops into smaller pieces – Merge multiple sub-ops into one • Recent/Current trends – Deeper pipelines (more and more stages) – Pipelining of memory accesses – Multiple different pipelines/sub-pipelines Spring 2016 :: CSE 502 – Computer Architecture Balancing Pipeline Stages (2/2) Coarser-Grained Machine Cycle: Finer-Grained Machine Cycle: 4 machine cyc / instruction 11 machine cyc /instruction IF IF TIF&ID= 8 units ID IF ID OF TOF= 9 units OF OF # stages = 4 # stages = 11 OF Tcyc= 9 units EX T = 5 units Tcyc= 3 units EX EX EX WB WB TOS= 9 units WB WB Spring 2016 :: CSE 502 – Computer Architecture Pipeline Examples AMDAHL 470V/7 IF_STEP PC GEN MIPS R2000/R3000 Cache Read IF_STEP Cache Read IF ID_STEP ID_STEP Decode OF_STEP Read REG OF_STEP RD Addr GEN EX_STEP ALU Cache Read Cache Read RS_STEP MEM EX_STEP EX 1 EX 2 WB RS_STEP Check Result Write Result Spring 2016 :: CSE 502 – Computer Architecture Instruction Dependencies (1/2) • Data Dependence – Read-After-Write (RAW) (the only true dependence) • Read must wait until earlier write finishes – Anti-Dependence (WAR) • Write must wait until earlier read finishes (avoid clobbering) – Output Dependence (WAW) • Earlier write can’t overwrite later write • Control Dependence (a.k.a. Procedural Dependence) – Branch condition must execute before branch target – Instructions after branch
Recommended publications
  • PIPELINING and ASSOCIATED TIMING ISSUES Introduction: While
    PIPELINING AND ASSOCIATED TIMING ISSUES Introduction: While studying sequential circuits, we studied about Latches and Flip Flops. While Latches formed the heart of a Flip Flop, we have explored the use of Flip Flops in applications like counters, shift registers, sequence detectors, sequence generators and design of Finite State machines. Another important application of latches and flip flops is in pipelining combinational/algebraic operation. To understand what is pipelining consider the following example. Let us take a simple calculation which has three operations to be performed viz. 1. add a and b to get (a+b), 2. get magnitude of (a+b) and 3. evaluate log |(a + b)|. Each operation would consume a finite period of time. Let us assume that each operation consumes 40 nsec., 35 nsec. and 60 nsec. respectively. The process can be represented pictorially as in Fig. 1. Consider a situation when we need to carry this out for a set of 100 such pairs. In a normal course when we do it one by one it would take a total of 100 * 135 = 13,500 nsec. We can however reduce this time by the realization that the whole process is a sequential process. Let the values to be evaluated be a1 to a100 and the corresponding values to be added be b1 to b100. Since the operations are sequential, we can first evaluate (a1 + b1) while the value |(a1 + b1)| is being evaluated the unit evaluating the sum is dormant and we can use it to evaluate (a2 + b2) giving us both |(a1 + b1)| and (a2 + b2) at the end of another evaluation period.
    [Show full text]
  • 2.5 Classification of Parallel Computers
    52 // Architectures 2.5 Classification of Parallel Computers 2.5 Classification of Parallel Computers 2.5.1 Granularity In parallel computing, granularity means the amount of computation in relation to communication or synchronisation Periods of computation are typically separated from periods of communication by synchronization events. • fine level (same operations with different data) ◦ vector processors ◦ instruction level parallelism ◦ fine-grain parallelism: – Relatively small amounts of computational work are done between communication events – Low computation to communication ratio – Facilitates load balancing 53 // Architectures 2.5 Classification of Parallel Computers – Implies high communication overhead and less opportunity for per- formance enhancement – If granularity is too fine it is possible that the overhead required for communications and synchronization between tasks takes longer than the computation. • operation level (different operations simultaneously) • problem level (independent subtasks) ◦ coarse-grain parallelism: – Relatively large amounts of computational work are done between communication/synchronization events – High computation to communication ratio – Implies more opportunity for performance increase – Harder to load balance efficiently 54 // Architectures 2.5 Classification of Parallel Computers 2.5.2 Hardware: Pipelining (was used in supercomputers, e.g. Cray-1) In N elements in pipeline and for 8 element L clock cycles =) for calculation it would take L + N cycles; without pipeline L ∗ N cycles Example of good code for pipelineing: §doi =1 ,k ¤ z ( i ) =x ( i ) +y ( i ) end do ¦ 55 // Architectures 2.5 Classification of Parallel Computers Vector processors, fast vector operations (operations on arrays). Previous example good also for vector processor (vector addition) , but, e.g. recursion – hard to optimise for vector processors Example: IntelMMX – simple vector processor.
    [Show full text]
  • Microprocessor Architecture
    EECE416 Microcomputer Fundamentals Microprocessor Architecture Dr. Charles Kim Howard University 1 Computer Architecture Computer System CPU (with PC, Register, SR) + Memory 2 Computer Architecture •ALU (Arithmetic Logic Unit) •Binary Full Adder 3 Microprocessor Bus 4 Architecture by CPU+MEM organization Princeton (or von Neumann) Architecture MEM contains both Instruction and Data Harvard Architecture Data MEM and Instruction MEM Higher Performance Better for DSP Higher MEM Bandwidth 5 Princeton Architecture 1.Step (A): The address for the instruction to be next executed is applied (Step (B): The controller "decodes" the instruction 3.Step (C): Following completion of the instruction, the controller provides the address, to the memory unit, at which the data result generated by the operation will be stored. 6 Harvard Architecture 7 Internal Memory (“register”) External memory access is Very slow For quicker retrieval and storage Internal registers 8 Architecture by Instructions and their Executions CISC (Complex Instruction Set Computer) Variety of instructions for complex tasks Instructions of varying length RISC (Reduced Instruction Set Computer) Fewer and simpler instructions High performance microprocessors Pipelined instruction execution (several instructions are executed in parallel) 9 CISC Architecture of prior to mid-1980’s IBM390, Motorola 680x0, Intel80x86 Basic Fetch-Execute sequence to support a large number of complex instructions Complex decoding procedures Complex control unit One instruction achieves a complex task 10
    [Show full text]
  • Instruction Pipelining (1 of 7)
    Chapter 5 A Closer Look at Instruction Set Architectures Objectives • Understand the factors involved in instruction set architecture design. • Gain familiarity with memory addressing modes. • Understand the concepts of instruction- level pipelining and its affect upon execution performance. 5.1 Introduction • This chapter builds upon the ideas in Chapter 4. • We present a detailed look at different instruction formats, operand types, and memory access methods. • We will see the interrelation between machine organization and instruction formats. • This leads to a deeper understanding of computer architecture in general. 5.2 Instruction Formats (1 of 31) • Instruction sets are differentiated by the following: – Number of bits per instruction. – Stack-based or register-based. – Number of explicit operands per instruction. – Operand location. – Types of operations. – Type and size of operands. 5.2 Instruction Formats (2 of 31) • Instruction set architectures are measured according to: – Main memory space occupied by a program. – Instruction complexity. – Instruction length (in bits). – Total number of instructions in the instruction set. 5.2 Instruction Formats (3 of 31) • In designing an instruction set, consideration is given to: – Instruction length. • Whether short, long, or variable. – Number of operands. – Number of addressable registers. – Memory organization. • Whether byte- or word addressable. – Addressing modes. • Choose any or all: direct, indirect or indexed. 5.2 Instruction Formats (4 of 31) • Byte ordering, or endianness, is another major architectural consideration. • If we have a two-byte integer, the integer may be stored so that the least significant byte is followed by the most significant byte or vice versa. – In little endian machines, the least significant byte is followed by the most significant byte.
    [Show full text]
  • Review of Computer Architecture
    Basic Computer Architecture CSCE 496/896: Embedded Systems Witawas Srisa-an Review of Computer Architecture Credit: Most of the slides are made by Prof. Wayne Wolf who is the author of the textbook. I made some modifications to the note for clarity. Assume some background information from CSCE 430 or equivalent von Neumann architecture Memory holds data and instructions. Central processing unit (CPU) fetches instructions from memory. Separate CPU and memory distinguishes programmable computer. CPU registers help out: program counter (PC), instruction register (IR), general- purpose registers, etc. von Neumann Architecture Memory Unit Input CPU Output Unit Control + ALU Unit CPU + memory address 200PC memory data CPU 200 ADD r5,r1,r3 ADD IRr5,r1,r3 Recalling Pipelining Recalling Pipelining What is a potential Problem with von Neumann Architecture? Harvard architecture address data memory data PC CPU address program memory data von Neumann vs. Harvard Harvard can’t use self-modifying code. Harvard allows two simultaneous memory fetches. Most DSPs (e.g Blackfin from ADI) use Harvard architecture for streaming data: greater memory bandwidth. different memory bit depths between instruction and data. more predictable bandwidth. Today’s Processors Harvard or von Neumann? RISC vs. CISC Complex instruction set computer (CISC): many addressing modes; many operations. Reduced instruction set computer (RISC): load/store; pipelinable instructions. Instruction set characteristics Fixed vs. variable length. Addressing modes. Number of operands. Types of operands. Tensilica Xtensa RISC based variable length But not CISC Programming model Programming model: registers visible to the programmer. Some registers are not visible (IR). Multiple implementations Successful architectures have several implementations: varying clock speeds; different bus widths; different cache sizes, associativities, configurations; local memory, etc.
    [Show full text]
  • CS152: Computer Systems Architecture Pipelining
    CS152: Computer Systems Architecture Pipelining Sang-Woo Jun Winter 2021 Large amount of material adapted from MIT 6.004, “Computation Structures”, Morgan Kaufmann “Computer Organization and Design: The Hardware/Software Interface: RISC-V Edition”, and CS 152 Slides by Isaac Scherson Eight great ideas ❑ Design for Moore’s Law ❑ Use abstraction to simplify design ❑ Make the common case fast ❑ Performance via parallelism ❑ Performance via pipelining ❑ Performance via prediction ❑ Hierarchy of memories ❑ Dependability via redundancy But before we start… Performance Measures ❑ Two metrics when designing a system 1. Latency: The delay from when an input enters the system until its associated output is produced 2. Throughput: The rate at which inputs or outputs are processed ❑ The metric to prioritize depends on the application o Embedded system for airbag deployment? Latency o General-purpose processor? Throughput Performance of Combinational Circuits ❑ For combinational logic o latency = tPD o throughput = 1/t F and G not doing work! PD Just holding output data X F(X) X Y G(X) H(X) Is this an efficient way of using hardware? Source: MIT 6.004 2019 L12 Pipelined Circuits ❑ Pipelining by adding registers to hold F and G’s output o Now F & G can be working on input Xi+1 while H is performing computation on Xi o A 2-stage pipeline! o For input X during clock cycle j, corresponding output is emitted during clock j+2. Assuming ideal registers Assuming latencies of 15, 20, 25… 15 Y F(X) G(X) 20 H(X) 25 Source: MIT 6.004 2019 L12 Pipelined Circuits 20+25=45 25+25=50 Latency Throughput Unpipelined 45 1/45 2-stage pipelined 50 (Worse!) 1/25 (Better!) Source: MIT 6.004 2019 L12 Pipeline conventions ❑ Definition: o A well-formed K-Stage Pipeline (“K-pipeline”) is an acyclic circuit having exactly K registers on every path from an input to an output.
    [Show full text]
  • Threading SIMD and MIMD in the Multicore Context the Ultrasparc T2
    Overview SIMD and MIMD in the Multicore Context Single Instruction Multiple Instruction ● (note: Tute 02 this Weds - handouts) ● Flynn’s Taxonomy Single Data SISD MISD ● multicore architecture concepts Multiple Data SIMD MIMD ● for SIMD, the control unit and processor state (registers) can be shared ■ hardware threading ■ SIMD vs MIMD in the multicore context ● however, SIMD is limited to data parallelism (through multiple ALUs) ■ ● T2: design features for multicore algorithms need a regular structure, e.g. dense linear algebra, graphics ■ SSE2, Altivec, Cell SPE (128-bit registers); e.g. 4×32-bit add ■ system on a chip Rx: x x x x ■ 3 2 1 0 execution: (in-order) pipeline, instruction latency + ■ thread scheduling Ry: y3 y2 y1 y0 ■ caches: associativity, coherence, prefetch = ■ memory system: crossbar, memory controller Rz: z3 z2 z1 z0 (zi = xi + yi) ■ intermission ■ design requires massive effort; requires support from a commodity environment ■ speculation; power savings ■ massive parallelism (e.g. nVidia GPGPU) but memory is still a bottleneck ■ OpenSPARC ● multicore (CMT) is MIMD; hardware threading can be regarded as MIMD ● T2 performance (why the T2 is designed as it is) ■ higher hardware costs also includes larger shared resources (caches, TLBs) ● the Rock processor (slides by Andrew Over; ref: Tremblay, IEEE Micro 2009 ) needed ⇒ less parallelism than for SIMD COMP8320 Lecture 2: Multicore Architecture and the T2 2011 ◭◭◭ • ◮◮◮ × 1 COMP8320 Lecture 2: Multicore Architecture and the T2 2011 ◭◭◭ • ◮◮◮ × 3 Hardware (Multi)threading The UltraSPARC T2: System on a Chip ● recall concurrent execution on a single CPU: switch between threads (or ● OpenSparc Slide Cast Ch 5: p79–81,89 processes) requires the saving (in memory) of thread state (register values) ● aggressively multicore: 8 cores, each with 8-way hardware threading (64 virtual ■ motivation: utilize CPU better when thread stalled for I/O (6300 Lect O1, p9–10) CPUs) ■ what are the costs? do the same for smaller stalls? (e.g.
    [Show full text]
  • Pipelined Computations
    Pipelined Computations 1 Functional Decomposition car manufacturing with three plants speedup for n inputs in a p-stage pipeline 2 Pipeline Implementations processors in a ring topology pipelined addition pipelined addition with MPI MCS 572 Lecture 27 Introduction to Supercomputing Jan Verschelde, 15 March 2021 Introduction to Supercomputing (MCS 572) Pipelined Computations L-27 15 March 2021 1 / 27 Pipelined Computations 1 Functional Decomposition car manufacturing with three plants speedup for n inputs in a p-stage pipeline 2 Pipeline Implementations processors in a ring topology pipelined addition pipelined addition with MPI Introduction to Supercomputing (MCS 572) Pipelined Computations L-27 15 March 2021 2 / 27 car manufacturing Consider a simplified car manufacturing process in three stages: (1) assemble exterior, (2) fix interior, and (3) paint and finish: input sequence - - - - c5 c4 c3 c2 c1 P1 P2 P3 The corresponding space-time diagram is below: space 6 P3 c1 c2 c3 c4 c5 P2 c1 c2 c3 c4 c5 P1 c1 c2 c3 c4 c5 - time After 3 time units, one car per time unit is completed. Introduction to Supercomputing (MCS 572) Pipelined Computations L-27 15 March 2021 3 / 27 p-stage pipelines A pipeline with p processors is a p-stage pipeline. Suppose every process takes one time unit to complete. How long till a p-stage pipeline completes n inputs? A p-stage pipeline on n inputs: After p time units the first input is done. Then, for the remaining n − 1 items, the pipeline completes at a rate of one item per time unit. ) p + n − 1 time units for the p-stage pipeline to complete n inputs.
    [Show full text]
  • Trends in Processor Architecture
    A. González Trends in Processor Architecture Trends in Processor Architecture Antonio González Universitat Politècnica de Catalunya, Barcelona, Spain 1. Past Trends Processors have undergone a tremendous evolution throughout their history. A key milestone in this evolution was the introduction of the microprocessor, term that refers to a processor that is implemented in a single chip. The first microprocessor was introduced by Intel under the name of Intel 4004 in 1971. It contained about 2,300 transistors, was clocked at 740 KHz and delivered 92,000 instructions per second while dissipating around 0.5 watts. Since then, practically every year we have witnessed the launch of a new microprocessor, delivering significant performance improvements over previous ones. Some studies have estimated this growth to be exponential, in the order of about 50% per year, which results in a cumulative growth of over three orders of magnitude in a time span of two decades [12]. These improvements have been fueled by advances in the manufacturing process and innovations in processor architecture. According to several studies [4][6], both aspects contributed in a similar amount to the global gains. The manufacturing process technology has tried to follow the scaling recipe laid down by Robert N. Dennard in the early 1970s [7]. The basics of this technology scaling consists of reducing transistor dimensions by a factor of 30% every generation (typically 2 years) while keeping electric fields constant. The 30% scaling in the dimensions results in doubling the transistor density (doubling transistor density every two years was predicted in 1975 by Gordon Moore and is normally referred to as Moore’s Law [21][22]).
    [Show full text]
  • Reverse Engineering X86 Processor Microcode
    Reverse Engineering x86 Processor Microcode Philipp Koppe, Benjamin Kollenda, Marc Fyrbiak, Christian Kison, Robert Gawlik, Christof Paar, and Thorsten Holz, Ruhr-University Bochum https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/koppe This paper is included in the Proceedings of the 26th USENIX Security Symposium August 16–18, 2017 • Vancouver, BC, Canada ISBN 978-1-931971-40-9 Open access to the Proceedings of the 26th USENIX Security Symposium is sponsored by USENIX Reverse Engineering x86 Processor Microcode Philipp Koppe, Benjamin Kollenda, Marc Fyrbiak, Christian Kison, Robert Gawlik, Christof Paar, and Thorsten Holz Ruhr-Universitat¨ Bochum Abstract hardware modifications [48]. Dedicated hardware units to counter bugs are imperfect [36, 49] and involve non- Microcode is an abstraction layer on top of the phys- negligible hardware costs [8]. The infamous Pentium fdiv ical components of a CPU and present in most general- bug [62] illustrated a clear economic need for field up- purpose CPUs today. In addition to facilitate complex and dates after deployment in order to turn off defective parts vast instruction sets, it also provides an update mechanism and patch erroneous behavior. Note that the implementa- that allows CPUs to be patched in-place without requiring tion of a modern processor involves millions of lines of any special hardware. While it is well-known that CPUs HDL code [55] and verification of functional correctness are regularly updated with this mechanism, very little is for such processors is still an unsolved problem [4, 29]. known about its inner workings given that microcode and the update mechanism are proprietary and have not been Since the 1970s, x86 processor manufacturers have throughly analyzed yet.
    [Show full text]
  • Chap. 9 Pipeline and Vector Processing
    9-1 Chap. 9 Pipeline and Vector Processing 9-1 Parallel Processing Simultaneous data processing tasks for the purpose of increasing the = computational speed Perform concurrent data processing to achieve faster execution time Multiple Functional Unit : Fig. 9-1 Parallel Processing Example Separate the execution unit into eight functional units operating in parallel Computer Architectural Classification Adder-subtractor Data-Instruction Stream : Flynn Integer multiply Serial versus Parallel Processing : Feng Parallelism and Pipelining : Händler Logic unit Flynn’s Classification Shift unit To Memory 1) SISD (Single Instruction - Single Data stream) Incrementer Processor registers » for practical purpose: only one processor is useful Floatint-point add-subtract » Example systems : Amdahl 470V/6, IBM 360/91 Floatint-point IS multiply Floatint-point divide CU PU MM IS DS © Korea Univ. of Tech. & Edu. Computer System Architecture Chap. 9 Pipeline and Vector Processing Dept. of Info. & Comm. 9-2 2) SIMD Shared memmory (Single Instruction - Multiple Data stream) DS1 PU1 MM1 » vector or array operations 에 적합한 형태 one vector operation includes many DS2 operations on a data stream PU2 MM2 IS » Example systems : CRAY -1, ILLIAC-IV CU DSn PUn MMn IS 3) MISD (Multiple Instruction - Single Data stream) » Data Stream에 Bottle neck으로 인해 DS IS1 IS1 실제로 사용되지 않음 CU1 PU1 Shared memory IS2 IS2 CU2 PU2 MMn MM2 MM1 ISn ISn CUn PUn DS © Korea Univ. of Tech. & Edu. Computer System Architecture Chap. 9 Pipeline and Vector Processing Dept. of Info. & Comm. 9-3 4) MIMD (Multiple Instruction - Multiple Data stream) » 대부분의 Multiprocessor Shared memory IS1 IS1 DS System에서 사용됨 CU1 PU1 MM1 » Shared memory or IS2 IS2 Message passing : Chap.
    [Show full text]
  • Chapter 4 Data-Level Parallelism in Vector, SIMD, and GPU Architectures
    Computer Architecture A Quantitative Approach, Fifth Edition Chapter 4 Data-Level Parallelism in Vector, SIMD, and GPU Architectures Copyright © 2012, Elsevier Inc. All rights reserved. 1 Contents 1. SIMD architecture 2. Vector architectures optimizations: Multiple Lanes, Vector Length Registers, Vector Mask Registers, Memory Banks, Stride, Scatter-Gather, 3. Programming Vector Architectures 4. SIMD extensions for media apps 5. GPUs – Graphical Processing Units 6. Fermi architecture innovations 7. Examples of loop-level parallelism 8. Fallacies Copyright © 2012, Elsevier Inc. All rights reserved. 2 Classes of Computers Classes Flynn’s Taxonomy SISD - Single instruction stream, single data stream SIMD - Single instruction stream, multiple data streams New: SIMT – Single Instruction Multiple Threads (for GPUs) MISD - Multiple instruction streams, single data stream No commercial implementation MIMD - Multiple instruction streams, multiple data streams Tightly-coupled MIMD Loosely-coupled MIMD Copyright © 2012, Elsevier Inc. All rights reserved. 3 Introduction Advantages of SIMD architectures 1. Can exploit significant data-level parallelism for: 1. matrix-oriented scientific computing 2. media-oriented image and sound processors 2. More energy efficient than MIMD 1. Only needs to fetch one instruction per multiple data operations, rather than one instr. per data op. 2. Makes SIMD attractive for personal mobile devices 3. Allows programmers to continue thinking sequentially SIMD/MIMD comparison. Potential speedup for SIMD twice that from MIMID! x86 processors expect two additional cores per chip per year SIMD width to double every four years Copyright © 2012, Elsevier Inc. All rights reserved. 4 Introduction SIMD parallelism SIMD architectures A. Vector architectures B. SIMD extensions for mobile systems and multimedia applications C.
    [Show full text]