Design of Digital Circuits Lecture 20: SIMD Processors

Prof. Onur Mutlu ETH Zurich Spring 2017 11 May 2017

Agenda for Today & Next Few Lectures

! Single-cycle Microarchitectures

! Multi-cycle and Microprogrammed Microarchitectures

! Pipelining

! Issues in Pipelining: Control & Data Dependence Handling, State Maintenance and Recovery, …

! Out-of-Order Execution

! Other Execution Paradigms

2 Readings for Today

! Lindholm et al., "NVIDIA Tesla: A Unified Graphics and Computing Architecture," IEEE Micro 2008.

3 Lecture Announcement

! May 15, 2017 ! 17:15-18:15

! HG F 30, Audi Max

! Onur Mutlu

! Inaugural Lecture

! Future Computing Architectures

! https://www.ethz.ch/en/news-and-events/events/ details.html?eventFeedId=35821

4 Other Approaches to Concurrency (or Instruction Level Parallelism)

Approaches to (Instruction-Level) Concurrency

! Pipelining ! Out-of-order execution

! Dataflow (at the ISA level)

! Superscalar Execution

! VLIW

! SIMD Processing (Vector and array processors, GPUs)

! Decoupled Access Execute

! Systolic Arrays

6 SIMD Processing: Exploiting Regular (Data) Parallelism

Review: SIMD Processing

! Single instruction operates on multiple data elements

" In time or in space

! Multiple processing elements

! Time-space duality

" Array processor: Instruction operates on multiple data elements at the same time using different spaces

" Vector processor: Instruction operates on multiple data elements in consecutive time steps using the same space

8 Review: Array vs. Vector Processors

ARRAY PROCESSOR VECTOR PROCESSOR

Instruction Stream Same op @ same time Different ops @ time LD VR # A[3:0] LD0 LD1 LD2 LD3 LD0 ADD VR # VR, 1 AD0 AD1 AD2 AD3 LD1 AD0 MUL VR # VR, 2 ST A[3:0] # VR MU0 MU1 MU2 MU3 LD2 AD1 MU0 ST0 ST1 ST2 ST3 LD3 AD2 MU1 ST0

Different ops @ same space AD3 MU2 ST1 MU3 ST2 Time Same op @ space ST3

Space Space

9 Review: SIMD Array Processing vs. VLIW

! VLIW: Multiple independent operations packed together by the compiler

10 Review: SIMD Array Processing vs. VLIW

! Array processor: Single operation on multiple (different) data elements

11 Review: Vector Processors

! A vector is a one-dimensional array of numbers ! Many scientific/commercial programs use vectors for (i = 0; i<=49; i++) C[i] = (A[i] + B[i]) / 2

! A vector processor is one whose instructions operate on vectors rather than scalar (single data) values

! Basic requirements

" Need to load/store vectors $ vector registers (contain vectors)

" Need to operate on vectors of different lengths $ vector length register (VLEN)

" Elements of a vector might be stored apart from each other in memory $ vector stride register (VSTR)

! Stride: distance between two elements of a vector

12 Review: Vector Processors (II)

! A vector instruction performs an operation on each element in consecutive cycles

" Vector functional units are pipelined

" Each pipeline stage operates on a different data element

! Vector instructions allow deeper pipelines

" No intra-vector dependencies $ no hardware interlocking needed within a vector

" No control flow within a vector

" Known stride allows easy address calculation for all vector elements

! Enables prefetching of vectors into registers/cache/memory

13 Vector Processor Advantages

+ No dependencies within a vector

" Pipelining. parallelization work really well

" Can have very deep pipelines, no dependencies!

+ Each instruction generates a lot of work

" Reduces instruction fetch bandwidth requirements

+ Highly regular memory access pattern

+ No need to explicitly code loops

" Fewer branches in the instruction sequence

14 Vector Processor Disadvantages -- Works (only) if parallelism is regular (data/SIMD parallelism) ++ Vector operations -- Very inefficient if parallelism is irregular -- How about searching for a key in a linked list?

Fisher, “Very Long Instruction Word architectures and the ELI-512,” ISCA 1983. 15 Vector Processor Limitations -- Memory (bandwidth) can easily become a bottleneck, especially if 1. compute/memory operation balance is not maintained 2. data is not mapped appropriately to memory banks

16 Vector Processing in More Depth

Vector Registers

! Each vector data register holds N M-bit values ! Vector control registers: VLEN, VSTR, VMASK

! Maximum VLEN can be N

" Maximum number of elements stored in a vector register

! Vector Mask Register (VMASK)

" Indicates which elements of vector to operate on

" Set by vector test instructions

! e.g., VMASK[i] = (Vk[i] == 0) M-bit wide M-bit wide V0,0 V1,0 V0,1 V1,1

V0,N-1 V1,N-1 18 Vector Functional Units

! Use deep pipeline to execute element operations $ fast clock cycle V V V 1 2 3

! Control of deep pipeline is simple because elements in vector are independent

Six stage multiply pipeline

V1 * V2 $ V3

Slide credit: Krste Asanovic 19 Vector Machine Organization (CRAY-1)

! CRAY-1 ! Russell, “The CRAY-1 computer system,” CACM 1978.

! Scalar and vector modes

! 8 64-element vector registers

! 64 bits per element ! 16 memory banks

! 8 64-bit scalar registers

! 8 24-bit address registers

20 Loading/Storing Vectors from/to Memory

! Requires loading/storing multiple elements

! Elements separated from each other by a constant distance (stride)

" Assume stride = 1 for now

! Elements can be loaded in consecutive cycles if we can start the load of one element per cycle

" Can sustain a throughput of one element per cycle

! Question: How do we achieve this with a memory that takes more than 1 cycle to access?

! Answer: Bank the memory; interleave the elements across banks 21 Memory Banking

! Memory is divided into banks that can be accessed independently; banks share address and data buses (to minimize pin cost)

! Can start and complete one bank access per cycle

! Can sustain N parallel accesses if all N go to different banks

Bank Bank Bank Bank 0 1 2 15

MDR MAR MDR MAR MDR MAR MDR MAR

Data bus

Address bus

CPU

Picture credit: Derek Chiou 22 Vector Memory System

! Next address = Previous address + Stride ! If stride = 1 & consecutive elements interleaved across banks & number of banks >= bank latency, then can sustain 1 element/cycle throughput Base Stride Vector Registers

Address Generator +

0 1 2 3 4 5 6 7 8 9 A B C D E F

Memory Banks

Picture credit: Krste Asanovic 23 Scalar Code Example

! For I = 0 to 49 " C[i] = (A[i] + B[i]) / 2

! Scalar code (instruction and its latency) MOVI R0 = 50 1 MOVA R1 = A 1 304 dynamic instructions MOVA R2 = B 1 MOVA R3 = C 1 X: LD R4 = MEM[R1++] 11 ;autoincrement addressing LD R5 = MEM[R2++] 11 ADD R6 = R4 + R5 4 SHFR R7 = R6 >> 1 1 ST MEM[R3++] = R7 11 DECBNZ R0, X 2 ;decrement and branch if NZ

24 Scalar Code Execution Time (In Order) ! Scalar execution time on an in-order processor with 1 bank

" First two loads in the loop cannot be pipelined: 2*11 cycles

" 4 + 50*40 = 2004 cycles

! Scalar execution time on an in-order processor with 16 banks (word-interleaved: consecutive words are stored in consecutive banks)

" First two loads in the loop can be pipelined

" 4 + 50*30 = 1504 cycles

! Why 16 banks?

" 11 cycle memory access latency

" Having 16 (>11) banks ensures there are enough banks to overlap enough memory operations to cover memory latency 25 Vectorizable Loops

! A loop is vectorizable if each iteration is independent of any other

! For I = 0 to 49

" C[i] = (A[i] + B[i]) / 2

! Vectorized loop (each instruction and its latency): MOVI VLEN = 50 1 7 dynamic instructions MOVI VSTR = 1 1 VLD V0 = A 11 + VLEN - 1 VLD V1 = B 11 + VLEN – 1 VADD V2 = V0 + V1 4 + VLEN - 1 VSHFR V3 = V2 >> 1 1 + VLEN - 1 VST C = V3 11 + VLEN – 1

26 Basic Vector Code Performance

! Assume no chaining (no vector data forwarding)

" i.e., output of a vector functional unit cannot be used as the direct input of another

" The entire vector register needs to be ready before any element of it can be used as part of another operation

! One memory port (one address generator)

! 16 memory banks (word-interleaved)

1 1 11 49 11 49 4 49 1 49 11 49

V0 = A[0..49] V1 = B[0..49] ADD SHIFT STORE

! 285 cycles

27 Vector Chaining

! Vector chaining: Data forwarding from one vector functional unit to another

V V V V V1 LV v1 2 3 4 5 MULV v3,v1,v2 ADDV v5, v3, v4

Chain Chain

Load Unit Mult. Add

Memory

Slide credit: Krste Asanovic 28 Vector Code Performance - Chaining

! Vector chaining: Data forwarding from one vector functional unit to another

1 1 11 49 11 49

Strict assumption: Each memory bank 4 49 has a single port (memory bandwidth bottleneck) These two VLDs cannot be 1 49 pipelined. WHY?

11 49

VLD and VST cannot be ! 182 cycles pipelined. WHY?

29 Vector Code Performance – Multiple Memory Ports

! Chaining and 2 load ports, 1 store port in each bank

1 1 11 49

1 11 49

4 49

1 49

11 49 ! 79 cycles

! 19X perf. improvement!

30 Questions (I)

! What if # data elements > # elements in a vector register?

" Idea: Break loops so that each iteration operates on # elements in a vector register

! E.g., 527 data elements, 64-element VREGs

! 8 iterations where VLEN = 64

! 1 iteration where VLEN = 15 (need to change value of VLEN)

" Called vector stripmining

! What if vector data is not stored in a strided fashion in memory? (irregular memory access to a vector)

" Idea: Use indirection to combine/pack elements into vector registers

" Called scatter/gather operations

31 Gather/Scatter Operations

Want to vectorize loops with indirect accesses: for (i=0; i

Indexed load instruction (Gather) LV vD, rD # Load indices in D vector LVI vC, rC, vD # Load indirect from rC base LV vB, rB # Load B vector ADDV.D vA,vB,vC # Do add SV vA, rA # Store result

32 Gather/Scatter Operations

! Gather/scatter operations often implemented in hardware to handle sparse vectors (matrices)

! Vector loads and stores use an index vector which is added to the base register to generate the addresses

Index Vector Data Vector (to Store) Stored Vector (in Memory)

0 3.14 Base+0 3.14 2 6.5 Base+1 X 6 71.2 Base+2 6.5 7 2.71 Base+3 X Base+4 X Base+5 X Base+6 71.2 Base+7 2.71

33 Conditional Operations in a Loop

! What if some operations should not be executed on a vector (based on a dynamically-determined condition)?

loop: for (i=0; i

! Idea: Masked operations

" VMASK register is a bit mask determining which data element should not be acted upon VLD V0 = A VLD V1 = B VMASK = (V0 != 0) VMUL V1 = V0 * V1 VST B = V1

" This is predicated execution. Execution is predicated on mask bit. 34 Another Example with Masking for (i = 0; i < 64; ++i) if (a[i] >= b[i]) Steps to execute the loop in SIMD code c[i] = a[i] else 1. Compare A, B to get VMASK c[i] = b[i] 2. Masked store of A into C

A B VMASK 3. Complement VMASK 1 2 0 2 2 1 4. Masked store of B into C 3 2 1 4 10 0 -5 -4 0 0 -3 1 6 5 1 -7 -8 1

35 Masked Vector Instructions

Simple Implementation Density-Time Implementation – execute all N operations, turn off – scan mask vector and only execute result writeback according to mask elements with non-zero masks

M[7]=1 A[7] B[7] M[7]=1

M[6]=0 A[6] B[6] M[6]=0 A[7] B[7] M[5]=1 A[5] B[5] M[5]=1 M[4]=1 A[4] B[4] M[4]=1 M[3]=0 A[3] B[3] M[3]=0 C[5] M[2]=0 C[4] M[1]=1 M[2]=0 C[2] M[0]=0 M[1]=1 C[1] C[1] Write data port

M[0]=0 C[0] Which one is better? Write Enable Write data port Tradeoffs?

Slide credit: Krste Asanovic 36 Some Issues

! Stride and banking

" As long as they are relatively prime to each other and there are enough banks to cover bank access latency, we can sustain 1 element/cycle throughput

! Storage of a matrix

" Row major: Consecutive elements in a row are laid out consecutively in memory

" Column major: Consecutive elements in a column are laid out consecutively in memory

" You need to change the stride when accessing a row versus column

37 38 Minimizing Bank Conflicts

! More banks

! Better data layout to match the access pattern

" Is this always possible?

! Better mapping of address to bank

" E.g., randomized mapping

" Rau, “Pseudo-randomly interleaved memory,” ISCA 1991.

39 Array vs. Vector Processors, Revisited

! Array vs. vector processor distinction is a “purist’s” distinction

! Most “modern” SIMD processors are a combination of both

" They exploit data parallelism in both time and space

" GPUs are a prime example we will cover in a bit more detail

40 Remember: Array vs. Vector Processors

ARRAY PROCESSOR VECTOR PROCESSOR

Different ops @ same space AD3 MU2 ST1 MU3 ST2 Time Same op @ space ST3

Space Space

41 Vector Instruction Execution VADD A,B $ C

Execution using Execution using one pipelined four pipelined functional unit functional units

A[6] B[6] A[24] B[24] A[25] B[25] A[26] B[26] A[27] B[27] A[5] B[5] A[20] B[20] A[21] B[21] A[22] B[22] A[23] B[23] A[4] B[4] A[16] B[16] A[17] B[17] A[18] B[18] A[19] B[19] A[3] B[3] A[12] B[12] A[13] B[13] A[14] B[14] A[15] B[15]

C[2] C[8] C[9] C[10] C[11]

C[1] C[4] C[5] C[6] C[7]

C[0] C[0] C[1] C[2] C[3]

Slide credit: Krste Asanovic 42 Vector Unit Structure Functional Unit

Partitioned Vector Registers Elements Elements Elements Elements 0, 4, 8, … 1, 5, 9, … 2, 6, 10, … 3, 7, 11, …

Lane

Memory Subsystem

Slide credit: Krste Asanovic 43 Vector Instruction Level Parallelism

Can overlap execution of multiple vector instructions

" Example machine has 32 elements per vector register and 8 lanes

" Completes 24 operations/cycle while issuing 1 vector instruction/cycle

Load Unit Multiply Unit Add Unit load mul add time load mul add

Instruction issue

Slide credit: Krste Asanovic 44

Design of Digital Circuits Lecture 20: SIMD Processors

Prof. Onur Mutlu ETH Zurich Spring 2017 11 May 2017

We did not cover the following slides in lecture. These are for your preparation for the next lecture. Automatic Code Vectorization for (i=0; i < N; i++) C[i] = A[i] + B[i]; Scalar Sequential Code Vectorized Code

load load load

Iter. 1 load load load

add Time add add

store store store

load Iter. Iter. Iter. 2 load 1 2 Vector Instruction

add Vectorization is a compile-time reordering of operation sequencing ⇒ requires extensive loop dependence analysis store Slide credit: Krste Asanovic 47 Vector/SIMD Processing Summary

! Vector/SIMD machines are good at exploiting regular data- level parallelism

" Same operation performed on many data elements

" Improve performance, simplify design (no intra-vector dependencies)

! Performance improvement limited by vectorizability of code

" Scalar operations limit vector machine performance

" Remember Amdahl’s Law

" CRAY-1 was the fastest SCALAR machine at its time!

! Many existing ISAs include (vector-like) SIMD operations

" Intel MMX/SSEn/AVX, PowerPC AltiVec, ARM Advanced SIMD

48 SIMD Operations in Modern ISAs

Intel Pentium MMX Operations

! Idea: One instruction operates on multiple data elements simultaneously

" Ala array processing (yet much more limited)

" Designed with multimedia (graphics) operations in mind

No VLEN register Opcode determines data type: 8 8-bit bytes 4 16-bit words 2 32-bit doublewords 1 64-bit quadword

Stride is always equal to 1.

Peleg and Weiser, “MMX Technology Extension to the Intel Architecture,” IEEE Micro, 1996.

50 MMX Example: Image Overlaying (I)

! Goal: Overlay the human in image 1 on top of the background in image 2

Peleg and Weiser, “MMX Technology Extension to the Intel Architecture,” IEEE Micro, 1996. 51 MMX Example: Image Overlaying (II)

Peleg and Weiser, “MMX Technology Extension to the Intel Architecture,” IEEE Micro, 1996. 52 GPUs (Graphics Processing Units)

GPUs are SIMD Engines Underneath

! The instruction pipeline operates like a SIMD pipeline (e.g., an array processor)

! However, the programming is done using threads, NOT SIMD instructions

! To understand this, let’s go back to our parallelizable code example

! But, before that, let’s distinguish between

" Programming Model (Software) vs.

" Execution Model (Hardware) 54 Programming Model vs. Hardware Execution Model

! Programming Model refers to how the programmer expresses the code

" E.g., Sequential (von Neumann), Data Parallel (SIMD), Dataflow, Multi-threaded (MIMD, SPMD), …

! Execution Model refers to how the hardware executes the code underneath

" E.g., Out-of-order execution, Vector processor, Array processor, Dataflow processor, Multiprocessor, Multithreaded processor, …

! Execution Model can be very different from the Programming Model

" E.g., von Neumann model implemented by an OoO processor

" E.g., SPMD model implemented by a SIMD processor (a GPU) 55 How Can You Exploit Parallelism Here? for (i=0; i < N; i++) C[i] = A[i] + B[i]; Scalar Sequential Code

load Let’s examine three programming load Iter. 1 options to exploit instruction-level

add parallelism present in this sequential code: store

load 1. Sequential (SISD)

Iter. 2 load 2. Data-Parallel (SIMD) add 3. Multithreaded (MIMD/SPMD) store 56 for (i=0; i < N; i++) Prog. Model 1: Sequential (SISD) C[i] = A[i] + B[i];

Scalar Sequential Code ! Can be executed on a:

load ! Pipelined processor Iter. 1 load ! Out-of-order execution processor add " Independent instructions executed when ready

store " Different iterations are present in the instruction window and can execute in load parallel in multiple functional units load Iter. 2 " In other words, the loop is dynamically unrolled by the hardware add ! Superscalar or VLIW processor

store " Can fetch and execute multiple instructions per cycle 57 for (i=0; i < N; i++) Prog. Model 2: Data Parallel (SIMD) C[i] = A[i] + B[i];

Scalar Sequential Code Vector Instruction Vectorized Code

load load VLD A $ V1 load Iter. 1 load load VLD B $ V2

add add VADD V1 + V2 $ V3

store store VST V3 $ C

load Iter. Iter. Realization: Each iteration is independent Iter. 2 1 load 2

Idea: Programmer or compiler generates a SIMD add instruction to execute the same instruction from all iterations across different data store Best executed by a SIMD processor (vector, array) 58 for (i=0; i < N; i++) Prog. Model 3: Multithreaded C[i] = A[i] + B[i];

Scalar Sequential Code

load load

Iter. 1 load load

add add

store store

load Iter. Iter. Realization: Each iteration is independent Iter. 2 1 load 2

Idea: Programmer or compiler generates a thread add to execute each iteration. Each thread does the same thing (but on different data) store Can be executed on a MIMD machine 59 for (i=0; i < N; i++) Prog. Model 3: Multithreaded C[i] = A[i] + B[i];

load load

add add

store store

Iter. Iter. 1 2 Realization: Each iteration is independent

Idea:This Programmer particular or model compiler is alsogenerates called: a thread to execute each iteration. Each thread does the sameSPMD: thing (butSingle on Programdifferent data) Multiple Data

CanCanCan be executed bebe executedexecuted on a MIMDonon aa SIMTSIMD machine machinemachine Single Instruction Multiple Thread 60 A GPU is a SIMD (SIMT) Machine

! Except it is not programmed using SIMD instructions

! It is programmed using threads (SPMD programming model)

" Each thread executes the same code but operates a different piece of data

" Each thread has its own context (i.e., can be treated/restarted/ executed independently)

! A set of threads executing the same instruction are dynamically grouped into a warp (wavefront) by the hardware

" A warp is essentially a SIMD operation formed by hardware!

61 for (i=0; i < N; i++) SPMD on SIMT Machine C[i] = A[i] + B[i];

load load Warp 0 at PC X

load load Warp 0 at PC X+1

add add Warp 0 at PC X+2

store store Warp 0 at PC X+3

Iter. Iter. 1 2 Warp: A set of threads that execute Realizationthe same: instructionEach iteration (i.e., is independentat the same PC)

Idea:This Programmer particular or model compiler is alsogenerates called: a thread to execute each iteration. Each thread does the sameSPMD: thing Single(but on Programdifferent data) Multiple Data

CanA GPUCan be executed beexecutes executed on it a using MIMDon a SIMDthe machine SIMT machine model: Single Instruction Multiple Thread 62 Graphics Processing Units SIMD not Exposed to Programmer (SIMT)

SIMD vs. SIMT Execution Model

! SIMD: A single sequential instruction stream of SIMD instructions $ each instruction specifies multiple data inputs

" [VLD, VLD, VADD, VST], VLEN

! SIMT: Multiple instruction streams of scalar instructions $ threads grouped dynamically into warps

" [LD, LD, ADD, ST], NumThreads

! Two Major SIMT Advantages:

" Can treat each thread separately $ i.e., can execute each thread independently (on any type of scalar pipeline) $ MIMD processing

" Can group threads into warps flexibly $ i.e., can group threads that are supposed to truly execute the same instruction $ dynamically obtain and maximize benefits of SIMD processing 64 for (i=0; i < N; i++) Multithreading of Warps C[i] = A[i] + B[i];

! Assume a warp consists of 32 threads ! If you have 32K iterations $ 1K warps

! Warps can be interleaved on the same pipeline $ Fine grained multithreading of warps

load load Warp 10 at PC X

load load

add add Warp 20 at PC X+2

store store

Iter.Iter. Iter.Iter. 33120*32 + 1 23420*32 + 2 65 Warps and Warp-Level FGMT

! Warp: A set of threads that execute the same instruction (on different data elements) $ SIMT (Nvidia-speak)

! All threads run the same code

! Warp: The threads that run lengthwise in a woven fabric …

Thread Warp 3 Thread Warp 8 Thread Warp Common PC Scalar Scalar Scalar Scalar Thread Warp 7 ThreadThread Thread Thread W X Y Z SIMD Pipeline

66 High-Level View of a GPU

67 Latency Hiding via Warp-Level FGMT

! Warp: A set of threads that execute the same instruction Warps available (on different data elements) Thread Warp 3 Thread Warp 8 for scheduling

Thread Warp 7 SIMD Pipeline ! Fine-grained multithreading I-Fetch " One instruction per thread in pipeline at a time (No Decode R R R F F F

interlocking)

" Interleave warp execution to A A A Warps accessing L L L

U U U memory hierarchy hide latencies Miss? ! Register values of all threads stay D-Cache Thread Warp 1 in register file All Hit? Data Thread Warp 2

! FGMT enables long latency Thread Warp 6 Writeback tolerance

" Millions of pixels

Slide credit: Tor Aamodt 68 Warp Execution (Recall the Slide) 32-thread warp executing ADD A[tid],B[tid] $ C[tid]

Execution using Execution using one pipelined four pipelined functional unit functional units

C[2] C[8] C[9] C[10] C[11]

C[1] C[4] C[5] C[6] C[7]

C[0] C[0] C[1] C[2] C[3]

Slide credit: Krste Asanovic 69 SIMD Execution Unit Structure Functional Unit

Registers for each Thread Registers for Registers for Registers for Registers for thread IDs thread IDs thread IDs thread IDs 0, 4, 8, … 1, 5, 9, … 2, 6, 10, … 3, 7, 11, …

Lane

Memory Subsystem

Slide credit: Krste Asanovic 70 Warp Instruction Level Parallelism

Can overlap execution of multiple instructions

" Example machine has 32 threads per warp and 8 lanes

" Completes 24 operations/cycle while issuing 1 warp/cycle

Load Unit Multiply Unit Add Unit W0 W1 W2 time W3 W4 W5

Warp issue

Slide credit: Krste Asanovic 71 SIMT Memory Access

! Same instruction in different threads uses thread id to index and access different data elements

Let’s assume N=16, 4 threads per warp $ 4 warps

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Threads + 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Data elements

+ + + +

Warp 0 Warp 1 Warp 2 Warp 3

Slide credit: Hyesoon Kim Sample GPU SIMT Code (Simplified)

CPU code for (ii = 0; ii < 100000; ++ii) { C[ii] = A[ii] + B[ii]; }

CUDA code // there are 100000 threads __global__ void KernelFunction(…) { int tid = blockDim.x * blockIdx.x + threadIdx.x; int varA = aa[tid]; int varB = bb[tid]; C[tid] = varA + varB; }

Slide credit: Hyesoon Kim Sample GPU Program (Less Simplified)

Slide credit: Hyesoon Kim 74 Warp-based SIMD vs. Traditional SIMD ! Traditional SIMD contains a single thread

" Lock step: a vector instruction needs to finish before another can start

" Programming model is SIMD (no extra threads) $ SW needs to know vector length

" ISA contains vector/SIMD instructions

! Warp-based SIMD consists of multiple scalar threads executing in a SIMD manner (i.e., same instruction executed by all threads)

" Does not have to be lock step

" Each thread can be treated individually (i.e., placed in a different warp) $ programming model not SIMD

! SW does not need to know vector length

! Enables multithreading and flexible dynamic grouping of threads

" ISA is scalar $ vector instructions can be formed dynamically

" Essentially, it is SPMD programming model implemented on SIMD hardware 75 SPMD

! Single procedure/program, multiple data

" This is a programming model rather than computer organization

! Each processing element executes the same procedure, except on different data elements

" Procedures can synchronize at certain points in program, e.g. barriers

! Essentially, multiple instruction streams execute the same program

" Each program/procedure 1) works on different data, 2) can execute a different control-flow path, at run-time

" Many scientific applications are programmed this way and run on MIMD hardware (multiprocessors)

" Modern GPUs programmed in a similar way on a SIMD hardware

76 SIMD vs. SIMT Execution Model

! SIMD: A single sequential instruction stream of SIMD instructions $ each instruction specifies multiple data inputs

" [VLD, VLD, VADD, VST], VLEN

! SIMT: Multiple instruction streams of scalar instructions $ threads grouped dynamically into warps

" [LD, LD, ADD, ST], NumThreads

! Two Major SIMT Advantages:

" Can treat each thread separately $ i.e., can execute each thread independently on any type of scalar pipeline $ MIMD processing

" Can group threads into warps flexibly $ i.e., can group threads that are supposed to truly execute the same instruction $ dynamically obtain and maximize benefits of SIMD processing 77 Threads Can Take Different Paths in Warp-based SIMD

! Each thread can have conditional control flow instructions ! Threads can execute different control flow paths

Thread Warp Common PC B Thread Thread Thread Thread C D F 1 2 3 4

Slide credit: Tor Aamodt 78 Control Flow Problem in GPUs/SIMT

! A GPU uses a SIMD pipeline to save area on control logic.

" Groups scalar threads into warps Branch

Path A ! Branch divergence occurs when threads Path B inside warps branch to different execution paths.

This is the same as conditional execution. Recall the Vector Mask and Masked Vector Operations?

Slide credit: Tor Aamodt 79 Remember: Each Thread Is Independent

! Two Major SIMT Advantages:

" Can treat each thread separately $ i.e., can execute each thread independently on any type of scalar pipeline $ MIMD processing

" Can group threads into warps flexibly $ i.e., can group threads that are supposed to truly execute the same instruction $ dynamically obtain and maximize benefits of SIMD processing

! If we have many threads

! We can find individual threads that are at the same PC

! And, group them together into a single warp dynamically ! This reduces “divergence” $ improves SIMD utilization

" SIMD utilization: fraction of SIMD lanes executing a useful operation (i.e., executing an active thread) 80 Dynamic Warp Formation/Merging

! Idea: Dynamically merge threads executing the same instruction (after branch divergence)

! Form new warps from warps that are waiting

" Enough threads branching to each path enables the creation of full new warps

Warp X Warp Z Warp Y

81 Dynamic Warp Formation/Merging

! Idea: Dynamically merge threads executing the same instruction (after branch divergence)

Branch

Path A

Path B

! Fung et al., “Dynamic Warp Formation and Scheduling for Efficient GPU Control Flow,” MICRO 2007. 82 Dynamic Warp Formation Example

x/1111 A y/1111 Legend x/1110 A A B y/0011 Execution of Warp x Execution of Warp y at Basic Block A at Basic Block A x/1000 x/0110 x/0001 C y/0010 D y/0001 F y/1100 D A new warp created from scalar E x/1110 threads of both Warp x and y y/0011 executing at Basic Block D x/1111 G y/1111 A A B B C C D D E E F F G G A A Baseline

Time Dynamic A A B B C D E E F G G A A Warp Formation Time

Slide credit: Tor Aamodt 83 Hardware Constraints Limit Flexibility of Warp Grouping Functional Unit

Registers for each Thread Registers for Registers for Registers for Registers for thread IDs thread IDs thread IDs thread IDs 0, 4, 8, … 1, 5, 9, … 2, 6, 10, … 3, 7, 11, …

Can you move any thread

Lane flexibly to any lane?

Memory Subsystem

Slide credit: Krste Asanovic 84 An Example GPU

NVIDIA GeForce GTX 285

! NVIDIA-speak:

" 240 stream processors

" “SIMT execution”

! Generic speak:

" 30 cores

" 8 SIMD functional units per core

86 Slide credit: Kayvon Fatahalian NVIDIA GeForce GTX 285 “core”

64 KB of storage … for thread contexts (registers)

= SIMD functional unit, control = instruction stream decode shared across 8 units