我使用NVIDIA Visual Profiler来分析我的代码。测试内核是：
//////////////////////////////////////////////////////////////// Group 1
static __global__ void gpu_test_divergency_0(float *a, float *b)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < 0)
{
a[tid] = tid;
}
else
{
b[tid] = tid;
}
}
static __global__ void gpu_test_divergency_1(float *a, float *b)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid == 0)
{
a[tid] = tid;
}
else
{
b[tid] = tid;
}
}
static __global__ void gpu_test_divergency_2(float *a, float *b)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid >= 0)
{
a[tid] = tid;
}
else
{
b[tid] = tid;
}
}
static __global__ void gpu_test_divergency_3(float *a, float *b)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid > 0)
{
a[tid] = tid;
}
else
{
b[tid] = tid;
}
}
//////////////////////////////////////////////////////////////// Group 2
static __global__ void gpu_test_divergency_4(float *a, float *b)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < 0)
{
a[tid] = tid + 1;
}
else
{
b[tid] = tid + 2;
}
}
static __global__ void gpu_test_divergency_5(float *a, float *b)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid == 0)
{
a[tid] = tid + 1;
}
else
{
b[tid] = tid + 2;
}
}
static __global__ void gpu_test_divergency_6(float *a, float *b)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid >= 0)
{
a[tid] = tid + 1;
}
else
{
b[tid] = tid + 2;
}
}
static __global__ void gpu_test_divergency_7(float *a, float *b)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid > 0)
{
a[tid] = tid + 1;
}
else
{
b[tid] = tid + 2;
}
}
//////////////////////////////////////////////////////////////// Group 3
static __global__ void gpu_test_divergency_8(float *a, float *b)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < 0)
{
a[tid] = tid + 1.0;
}
else
{
b[tid] = tid + 2.0;
}
}
static __global__ void gpu_test_divergency_9(float *a, float *b)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid == 0)
{
a[tid] = tid + 1.0;
}
else
{
b[tid] = tid + 2.0;
}
}
static __global__ void gpu_test_divergency_10(float *a, float *b)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid >= 0)
{
a[tid] = tid + 1.0;
}
else
{
b[tid] = tid + 2.0;
}
}
static __global__ void gpu_test_divergency_11(float *a, float *b)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid > 0)
{
a[tid] = tid + 1.0;
}
else
{
b[tid] = tid + 2.0;
}
}
当我使用＆lt;＆lt;＆lt;＆lt;＆lt;＆lt;＆lt;＆lt;＆lt;＆lt;＆lt; 1,32＆gt;＆gt;＆gt;，我从分析器得到了这样的结果：
gpu_test_divergency_0 : Branch Efficiency = 100% branch = 1 divergent branch = 0
gpu_test_divergency_1 : Branch Efficiency = 100% branch = 1 divergent branch = 0
gpu_test_divergency_2 : Branch Efficiency = 100% branch = 1 divergent branch = 0
gpu_test_divergency_3 : Branch Efficiency = 100% branch = 1 divergent branch = 0
gpu_test_divergency_4 : Branch Efficiency = 100% branch = 3 divergent branch = 0
gpu_test_divergency_5 : Branch Efficiency = 100% branch = 3 divergent branch = 0
gpu_test_divergency_6 : Branch Efficiency = 100% branch = 2 divergent branch = 0
gpu_test_divergency_7 : Branch Efficiency = 100% branch = 3 divergent branch = 0
gpu_test_divergency_8 : Branch Efficiency = 100% branch = 3 divergent branch = 0
gpu_test_divergency_9 : Branch Efficiency = 75% branch = 4 divergent branch = 1
gpu_test_divergency_10 : Branch Efficiency = 100% branch = 2 divergent branch = 0
gpu_test_divergency_11 : Branch Efficiency = 75% branch = 4 divergent branch = 1
当我使用＆lt;＆lt;＆lt;＆lt;＆lt;＆lt;＆lt;＆lt;＆lt;＆lt;＆lt; 1,64＆gt;＆gt;＆gt;，我从分析器得到了这样的结果：
gpu_test_divergency_0 : Branch Efficiency = 100% branch = 2 divergent branch = 0
gpu_test_divergency_1 : Branch Efficiency = 100% branch = 2 divergent branch = 0
gpu_test_divergency_2 : Branch Efficiency = 100% branch = 2 divergent branch = 0
gpu_test_divergency_3 : Branch Efficiency = 100% branch = 2 divergent branch = 0
gpu_test_divergency_4 : Branch Efficiency = 100% branch = 6 divergent branch = 0
gpu_test_divergency_5 : Branch Efficiency = 100% branch = 6 divergent branch = 0
gpu_test_divergency_6 : Branch Efficiency = 100% branch = 4 divergent branch = 0
gpu_test_divergency_7 : Branch Efficiency = 100% branch = 5 divergent branch = 0
gpu_test_divergency_8 : Branch Efficiency = 100% branch = 6 divergent branch = 0
gpu_test_divergency_9 : Branch Efficiency = 85.7% branch = 7 divergent branch = 1
gpu_test_divergency_10 : Branch Efficiency = 100% branch = 4 divergent branch = 0
gpu_test_divergency_11 : Branch Efficiency = 83.3% branch = 6 divergent branch = 1
我在Linux上使用CUDA Capability of 2.0和NVIDIA Visual Profiler v4.2的“GeForce GTX 570”。根据文件：
“branch” - “执行内核的线程占用的分支数。如果warp中至少有一个线程占用分支，则此计数器将加1。”
“divergent branch” - “warp中的发散分支数。如果warp中的至少一个胎面通过依赖于数据的方向发散（即，遵循不同的执行路径），则此计数器将增加1条件分支。“
但我对结果感到困惑。为什么每个测试组的“分支”数量不同？为什么只有第三个测试组似乎有正确的“分歧分支”？
@JackOLantern：我在发布模式下编译。我以你的方式拆卸它。 “gpu_test_divergency_4”的结果与您的结果完全相同，但“gpu_test_divergency_0”的结果不同：
Function : _Z21gpu_test_divergency_0PfS_
/*0000*/ /*0x00005de428004404*/ MOV R1, c [0x1] [0x100];
/*0008*/ /*0x94001c042c000000*/ S2R R0, SR_CTAid_X;
/*0010*/ /*0x84009c042c000000*/ S2R R2, SR_Tid_X;
/*0018*/ /*0x20009ca320044000*/ IMAD R2, R0, c [0x0] [0x8], R2;
/*0020*/ /*0xfc21dc23188e0000*/ ISETP.LT.AND P0, pt, R2, RZ, pt;
/*0028*/ /*0x0920de0418000000*/ I2F.F32.S32 R3, R2;
/*0030*/ /*0x9020204340004000*/ @!P0 ISCADD R0, R2, c [0x0] [0x24], 0x2;
/*0038*/ /*0x8020804340004000*/ @P0 ISCADD R2, R2, c [0x0] [0x20], 0x2;
/*0040*/ /*0x0000e08590000000*/ @!P0 ST [R0], R3;
/*0048*/ /*0x0020c08590000000*/ @P0 ST [R2], R3;
/*0050*/ /*0x00001de780000000*/ EXIT;
我想，就像你说的那样，转换指令（在这种情况下为I2F）不会添加额外的分支。
但我看不出这些反汇编代码与Profiler结果之间的关系。我从另一篇文章（https://devtalk.nvidia.com/default/topic/463316/branch-divergent-branches/）中了解到，使用SM上的实际线程（warp）运行情况计算出不同的分支。所以我想我们不能推断每个实际运行的分支差异，只是根据这些反汇编代码。我对吗？
答案 0 :(得分：1)
关注 - 使用投票本体来检查线索的分歧
我认为检查warp中线程差异的最佳方法是使用投票内在函数，尤其是__ballot和__popc内在函数。关于__ballot和__popc的一个很好的解释可以在Shane Cook，CUDA编程，Morgan Kaufmann的书中找到。
__ballot的原型如下
unsigned int __ballot(int predicate);
如果谓词不为零，__ballot会返回设置了N位的值，其中N为threadIdx.x。
另一方面，__popc返回使用32 - 位参数设置的位数。
因此，通过联合使用__ballot，__popc和atomicAdd，可以检查扭曲是否发散。
为此，我设置了以下代码
#include <cuda.h>
#include <stdio.h>
#include <iostream>
#include <cuda.h>
#include <cuda_runtime.h>
__device__ unsigned int __ballot_non_atom(int predicate)
{
if (predicate != 0) return (1 << (threadIdx.x % 32));
else return 0;
}
__global__ void gpu_test_divergency_0(unsigned int* d_ballot, int Num_Warps_per_Block)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
const unsigned int warp_num = threadIdx.x >> 5;
atomicAdd(&d_ballot[warp_num+blockIdx.x*Num_Warps_per_Block],__popc(__ballot_non_atom(tid > 2)));
// atomicAdd(&d_ballot[warp_num+blockIdx.x*Num_Warps_per_Block],__popc(__ballot(tid > 2)));
}
#include <conio.h>
int main(int argc, char *argv[])
{
unsigned int Num_Threads_per_Block = 64;
unsigned int Num_Blocks_per_Grid = 1;
unsigned int Num_Warps_per_Block = Num_Threads_per_Block/32;
unsigned int Num_Warps_per_Grid = (Num_Threads_per_Block*Num_Blocks_per_Grid)/32;
unsigned int* h_ballot = (unsigned int*)malloc(Num_Warps_per_Grid*sizeof(unsigned int));
unsigned int* d_ballot; cudaMalloc((void**)&d_ballot, Num_Warps_per_Grid*sizeof(unsigned int));
for (int i=0; i<Num_Warps_per_Grid; i++) h_ballot[i] = 0;
cudaMemcpy(d_ballot, h_ballot, Num_Warps_per_Grid*sizeof(unsigned int), cudaMemcpyHostToDevice);
gpu_test_divergency_0<<<Num_Blocks_per_Grid,Num_Threads_per_Block>>>(d_ballot,Num_Warps_per_Block);
cudaMemcpy(h_ballot, d_ballot, Num_Warps_per_Grid*sizeof(unsigned int), cudaMemcpyDeviceToHost);
for (int i=0; i<Num_Warps_per_Grid; i++) {
if ((h_ballot[i] == 0)||(h_ballot[i] == 32)) std::cout << "Warp " << i << " IS NOT divergent- Predicate true for " << h_ballot[i] << " threads\n";
else std::cout << "Warp " << i << " IS divergent - Predicate true for " << h_ballot[i] << " threads\n";
}
getch();
return EXIT_SUCCESS;
}
请注意，我现在正在运行计算能力1.2卡上的代码，因此在上面的示例中，我使用的是__ballot_non_atom，它是__ballot的非内在等价物，因为__ballot仅适用于计算能力＆gt; = 2.0。换句话说，如果您的计算能力＆gt; = 2.0，请在内核函数中使用__ballot取消注释该指令。
使用上面的代码，只需更改内核函数中的相关谓词，就可以使用上面的所有内核函数。
以前的答案
我在发布模式下编译了计算功能2.0的代码，并使用-keep保留了中间文件，并使用cuobjdump实用程序生成反汇编你的两个内核，即：
static __global__ void gpu_test_divergency_0(float *a, float *b)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < 0) a[tid] = tid;
else b[tid] = tid;
}
和
static __global__ void gpu_test_divergency_4(float *a, float *b)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < 0) a[tid] = tid + 1;
else b[tid] = tid + 2;
}
结果如下
gpu_test_divergency_0
/*0000*/ MOV R1, c[0x1][0x100]; /* 0x2800440400005de4 */
/*0008*/ S2R R0, SR_CTAID.X; /* 0x2c00000094001c04 */
/*0010*/ S2R R2, SR_TID.X; /* 0x2c00000084009c04 */
/*0018*/ IMAD R2, R0, c[0x0][0x8], R2; /* 0x2004400020009ca3 */
/*0020*/ ISETP.LT.AND P0, PT, R2, RZ, PT; /* 0x188e0000fc21dc23 */
/*0028*/ I2F.F32.S32 R0, R2; /* 0x1800000009201e04 */
/*0030*/ @!P0 ISCADD R3, R2, c[0x0][0x24], 0x2; /* 0x400040009020e043 */
/*0038*/ @P0 ISCADD R2, R2, c[0x0][0x20], 0x2; /* 0x4000400080208043 */
/*0040*/ @!P0 ST [R3], R0; /* 0x9000000000302085 */
/*0048*/ @P0 ST [R2], R0; /* 0x9000000000200085 */
/*0050*/ EXIT ; /* 0x8000000000001de7 */
和
gpu_test_divergency_4
/*0000*/ MOV R1, c[0x1][0x100]; /* 0x2800440400005de4 */
/*0008*/ S2R R0, SR_CTAID.X; /* 0x2c00000094001c04 */ R0 = BlockIdx.x
/*0010*/ S2R R2, SR_TID.X; /* 0x2c00000084009c04 */ R2 = ThreadIdx.x
/*0018*/ IMAD R0, R0, c[0x0][0x8], R2; /* 0x2004400020001ca3 */ R0 = R0 * c + R2
/*0020*/ ISETP.LT.AND P0, PT, R0, RZ, PT; /* 0x188e0000fc01dc23 */ If statement
/*0028*/ @P0 BRA.U 0x58; /* 0x40000000a00081e7 */ Branch 1 - Jump to 0x58
/*0030*/ @!P0 IADD R2, R0, 0x2; /* 0x4800c0000800a003 */ Branch 2 - R2 = R0 + 2
/*0038*/ @!P0 ISCADD R0, R0, c[0x0][0x24], 0x2; /* 0x4000400090002043 */ Branch 2 - Calculate gmem address
/*0040*/ @!P0 I2F.F32.S32 R2, R2; /* 0x180000000920a204 */ Branch 2 - R2 = R2 after int to float cast
/*0048*/ @!P0 ST [R0], R2; /* 0x900000000000a085 */ Branch 2 - gmem store
/*0050*/ @!P0 BRA.U 0x78; /* 0x400000008000a1e7 */ Branch 2 - Jump to 0x78 (exit)
/*0058*/ @P0 IADD R2, R0, 0x1; /* 0x4800c00004008003 */ Branch 1 - R2 = R0 + 1
/*0060*/ @P0 ISCADD R0, R0, c[0x0][0x20], 0x2; /* 0x4000400080000043 */ Branch 1 - Calculate gmem address
/*0068*/ @P0 I2F.F32.S32 R2, R2; /* 0x1800000009208204 */ Branch 1 - R2 = R2 after int to float cast
/*0070*/ @P0 ST [R0], R2; /* 0x9000000000008085 */ Branch 1 - gmem store
/*0078*/ EXIT ; /* 0x8000000000001de7 */
从上面的反汇编中，我预计你的分支发散测试的结果是一样的。
您是在调试还是发布模式下编译？