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ECE 569 High Performance Processors and Systems. Administrative HW2 due Thursday 2/13 @ start of class GPUs Conditional execution Current state of the art. GPUs == SIMD. Registers. Registers. Registers. ALU. ALU. ALU. RAM. . . . . . . Control Processor. Instruction Memory.
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ECE 569 High Performance Processors and Systems • Administrative • HW2 due Thursday 2/13 @ start of class • GPUs • Conditional execution • Current state of the art ECE 569 -- 11 Feb 2014
GPUs == SIMD Registers Registers Registers ALU ALU ALU RAM . . . . . . Control Processor Instruction Memory ECE 569 -- 11 Feb 2014
A Simple Execution Model • No branch prediction • Just evaluate branch targets and wait for resolution • But wait is only a small number of cycles once data is loaded from global memory • No speculation • Only execute useful instructions Lecture 07 ECE 569 -- 11 Feb 2014
Example: conditionals if (threadIdx >= 2) { out[threadIdx] += 100; } else { out[threadIdx] += 10; } compare threadIdx,2 Reg Instruction Unit Reg Reg ... P0 P! PM-1 Memory Lecture 07 ECE 569 -- 11 Feb 2014
then part if (threadIdx.x >= 2) { out[threadIdx.x] += 100; } else { out[threadIdx.x] += 10; } all threads in warp where condtrue do then part: load … add 100 store … other threads in warp are "masked off" and wait… X ✔ ✔ X Reg Instruction Unit Reg Reg ... P0 P! PM-1 Memory Lecture 07 ECE 569 -- 11 Feb 2014
else part if (threadIdx >= 2) { out[threadIdx] += 100; } else { out[threadIdx] += 10; } all threads in warp where condfalse do else part: load … add 10 store … other threads in warp are "masked off" and wait… ✔ ✔ X X Reg Instruction Unit Reg Reg ... P0 P! PM-1 Memory Lecture 07 ECE 569 -- 11 Feb 2014
Terminology • Divergent paths • Different threads within a warp take different control flow paths within a kernel function • N divergent paths in a warp? • result from nested conditionals… • An N-way divergent warp is serially issued over the N different paths using a hardware stack and per-thread predication logic to only write back results from the threads taking each divergent path. • Performance decreases by about a factor of N Lecture 07 ECE 569 -- 11 Feb 2014
Example: Vector Reduction Thread 0 Thread 2 Thread 4 Thread 6 Thread 8 Thread 10 0 1 2 3 4 5 6 7 8 9 10 11 1 0+1 2+3 4+5 6+7 8+9 10+11 2 0...3 4..7 8..11 3 0..7 8..15 iterations Array elements
Implementation unsigned int t = threadIdx.x; for (unsigned int stride = 1; stride < blockDim.x; stride *= 2) { __syncthreads(); if (t % (2*stride) == 0) d_out[t] += d_out[t+stride]; } Lecture 07 ECE 569 -- 11 Feb 2014
Some Observations • In each iteration, two control flow paths • Threads that perform addition and threads that do not • Threads that do not perform addition may cost extra cycles depending on the implementation of divergence • No more than half of threads will be executing at any time • All odd index threads are disabled right from the beginning! • On average, less than ¼ of the threads will be activated for all warps over time Lecture 07 ECE 569 -- 11 Feb 2014
A better approach Thread 0 0 1 2 3 … 13 14 15 16 17 18 19 1 0+16 15+31 3 4
A better implementation unsigned int t = threadIdx.x; for (unsigned int stride = blockDim.x >> 1; stride >= 1; stride >> 1) { __syncthreads(); if (t < stride) d_out[t] += d_out[t+stride]; } Lecture 07 ECE 569 -- 11 Feb 2014
Some Observations • Only the last 5 iterations will have divergence • within a warp • Entire warps will be shut down as iterations progress • For a 512-thread block, 4 iterations to shut down all but one warp in each block • Better resource utilization, will retire warps and blocks faster Lecture 07 ECE 569 -- 11 Feb 2014
Optimization: Predicated Execution <p1> LDR r1,r2,0 • If p1 is TRUE, instruction executes normally • If p1 is FALSE, instruction treated as NOP ECE 569 -- 11 Feb 2014
Predication Example : : if (X == 10) c = c + 1; : : : : LDR r5, X p1 <- r5 eq 10 <p1> LDR r1, C <p1> ADD r1, r1, 1 <p1> STR r1, C : : ECE 569 -- 11 Feb 2014
Predication optimizes execution of simple if-then-else A A B C D B C D ECE 569 -- 11 Feb 2014
Predication == better scheduling : : p1,p2 <- r5 eq 10 <p1> inst 1 from B <p1> inst 2 from B <p1> : : <p2> inst 1 from C <p2> inst 2 from C : : : : p1,p2 <- r5 eq 10 <p1> inst 1 from B <p2> inst 1 from C <p1> inst 2 from B <p2> inst 2 from C <p1> : : schedule ECE 569 -- 11 Feb 2014 17
State of the Art with GPUs Lecture 07 ECE 569 -- 11 Feb 2014 18
nVidiaKepler GK110 • 7.1B transistors • 2,880 cores • 2012 model :-) ECE 569 -- 11 Feb 2014
TITAN: 2nd fastest supercomputer 560,640 total cores: ½ CPU and ½ GPU CPUs: 18,688 AMD Opteron (16-core) GPUs: 18,688 nVidia Tesla K20x 710,144 GB RAM 8,000 kWatts of power ECE 569 -- 11 Feb 2014
