Skip to content

D
dlib
Commit d248a225 authored by Davis King's avatar Davis King
Browse files
Options

Added the launch_kernel() function that launches a kernel by smartly picking 

the number of threads and blocks rather than using the hard coded numbers I had
in there.  This makes some functions noticeably faster.

Also added a dot() function that is fully asynchronous.
parent 8466d332
Hide whitespace changes
Inline Side-by-side
Showing with 105 additions and 19 deletions
dlib/dnn/cuda_dlib.cu
View file @ d248a225
......@@ -80,16 +80,16 @@ namespace dlib
const auto s2 = src2.host();
if (dest.size() == src1.size() && src1.size() == src2.size())
{
_cuda_multiply1<<<512,512>>>(dest.device(), src1.device(), src2.device(), src1.size());
launch_kernel(_cuda_multiply1,max_jobs(dest.size()),dest.device(), src1.device(), src2.device(), src1.size());
}
else if (dest.num_samples() == 1)
{
_cuda_multiply2<<<512,512>>>(dest.device(), src1.device(), src2.device(),
launch_kernel(_cuda_multiply2,max_jobs(dest.size()),dest.device(), src1.device(), src2.device(),
dest.size(), src1.size(), src2.size(), max_size);
}
else
{
_cuda_multiply3<<<512,512>>>(dest.device(), src1.device(), src2.device(),
launch_kernel(_cuda_multiply3,max_jobs(dest.size()),dest.device(), src1.device(), src2.device(),
dest.size(), src1.size(), src2.size());
}
}
......@@ -150,23 +150,24 @@ namespace dlib
if (have_same_dimensions(dest, src1) &&
have_same_dimensions(dest, src2))
{
_cuda_add1<<<512,512>>>(dest.device(), src1.device(), src2.device(), dest.size());
launch_kernel(_cuda_add1,max_jobs(dest.size()), dest.device(), src1.device(), src2.device(), dest.size());
}
else
{
// Otherwise, do the more complex version with bounds checking.
_cuda_add2<<<512,512>>>(dest.device(), src1.device(), src2.device(),
dest.num_samples(), dest.k(), dest.nr(), dest.nc(),
src1.num_samples(), src1.k(), src1.nr(), src1.nc(),
src2.num_samples(), src2.k(), src2.nr(), src2.nc()
);
launch_kernel(_cuda_add2,max_jobs(dest.size()),
dest.device(), src1.device(), src2.device(),
dest.num_samples(), dest.k(), dest.nr(), dest.nc(),
src1.num_samples(), src1.k(), src1.nr(), src1.nc(),
src2.num_samples(), src2.k(), src2.nr(), src2.nc()
);
}
}
// ------------------------------------------------------------------------------------
__global__ void _cuda_affine_transform(float* d, const float* s, size_t n, float A, float B)
__global__ void _cuda_affine_transform1(float* d, const float* s, size_t n, float A, float B)
{
for (auto i : grid_stride_range(0, n))
{
......@@ -182,12 +183,12 @@ namespace dlib
)
{
DLIB_CASSERT(dest.size()==src.size(),"");
_cuda_affine_transform<<<512,512>>>(dest.device(), src.device(), src.size(), A, B);
launch_kernel(_cuda_affine_transform1,max_jobs(dest.size()),dest.device(), src.device(), src.size(), A, B);
}
// ----------------------------------------------------------------------------------------
__global__ void _cuda_affine_transform(float* d, const float* s1, const float* s2, size_t n, float A, float B, float C)
__global__ void _cuda_affine_transform4(float* d, const float* s1, const float* s2, size_t n, float A, float B, float C)
{
for (auto i : grid_stride_range(0, n))
{
......@@ -206,12 +207,12 @@ namespace dlib
{
DLIB_CASSERT(dest.size()==src1.size(),"");
DLIB_CASSERT(dest.size()==src2.size(),"");
_cuda_affine_transform<<<512,512>>>(dest.device(), src1.device(), src2.device(), dest.size(), A, B, C);
launch_kernel(_cuda_affine_transform4,max_jobs(dest.size()),dest.device(), src1.device(), src2.device(), dest.size(), A, B, C);
}
// ----------------------------------------------------------------------------------------
__global__ void _cuda_affine_transform(
__global__ void _cuda_affine_transform5(
float* d, const float* s1, const float* s2, const float* s3, size_t n, float A, float B, float C, float D
)
{
......@@ -235,7 +236,7 @@ namespace dlib
DLIB_CASSERT(dest.size()==src1.size(),"");
DLIB_CASSERT(dest.size()==src2.size(),"");
DLIB_CASSERT(dest.size()==src3.size(),"");
_cuda_affine_transform<<<512,512>>>(dest.device(), src1.device(),
launch_kernel(_cuda_affine_transform5,max_jobs(dest.size()),dest.device(), src1.device(),
src2.device(), src3.device(), dest.size(), A, B, C, D);
}
......@@ -273,11 +274,11 @@ namespace dlib
if (A.num_samples() == 1)
{
_cuda_affine_transform3<<<512,512>>>(dest.device(), src.device(), src.size(), A.device(), B.device(), A.size());
launch_kernel(_cuda_affine_transform3,max_jobs(dest.size()),dest.device(), src.device(), src.size(), A.device(), B.device(), A.size());
}
else
{
_cuda_affine_transform2<<<512,512>>>(dest.device(), src.device(), src.size(), A.device(), B.device());
launch_kernel(_cuda_affine_transform2,max_jobs(dest.size()),dest.device(), src.device(), src.size(), A.device(), B.device());
}
}
......@@ -305,7 +306,7 @@ namespace dlib
gradient_input.nc() == grad.nc() &&
gradient_input.size() > 0,"");
_add_bias_gradient<<<512,512>>>(grad.device(), gradient_input.device(), grad.size(), gradient_input.size());
launch_kernel(_add_bias_gradient,max_jobs(grad.size()),grad.device(), gradient_input.device(), grad.size(), gradient_input.size());
}
// -----------------------------------------------------------------------------------
......@@ -324,11 +325,37 @@ namespace dlib
float thresh
)
{
_cuda_threshold<<<512,512>>>(data.device(), data.size(), thresh);
launch_kernel(_cuda_threshold,max_jobs(data.size()),data.device(), data.size(), thresh);
}
// ------------------------------------------------------------------------------------
__global__ void _cuda_dot(const float* a, const float* b, size_t n, float* result)
{
// Parallel sum everything into local temp variables.
float temp = 0;
for(auto i : grid_stride_range(0, n))
temp += a[i]*b[i];
// Then do the warp reduce add thing to merge into one output value.
warp_reduce_atomic_add(*result, temp);
}
void dot (
const tensor& a,
const tensor& b,
tensor& result,
size_t idx
)
{
DLIB_CASSERT(a.size() == b.size(), "");
DLIB_CASSERT(idx < result.size(), "");
launch_kernel(_cuda_dot, max_jobs(a.size()), a.device(), b.device(), a.size(), result.device()+idx);
}
// ----------------------------------------------------------------------------------------
}
}
dlib/dnn/cuda_dlib.h
View file @ d248a225
......@@ -89,6 +89,15 @@ namespace dlib
float thresh
);
// ----------------------------------------------------------------------------------------
void dot (
const tensor& a,
const tensor& b,
tensor& result,
size_t idx
);
// ------------------------------------------------------------------------------------
// ------------------------------------------------------------------------------------
// ------------------------------------------------------------------------------------
......
dlib/dnn/cuda_utils.h
View file @ d248a225
......@@ -127,6 +127,56 @@ namespace dlib
atomicAdd(&out, val);
}
// ------------------------------------------------------------------------------------
struct max_jobs
{
max_jobs(size_t n) : num(n) {}
size_t num;
};
template <typename Kernel, typename... T>
void launch_kernel (
Kernel K,
T ...args
)
/*!
ensures
- launches the given kernel K(args...). The point of this function is to
automatically set the kernel launch parameters to something reasonable
based on the properties of the kernel and the current GPU card.
!*/
{
int num_blocks, num_threads;
CHECK_CUDA(cudaOccupancyMaxPotentialBlockSize(&num_blocks,&num_threads,K));
K<<<num_blocks,num_threads>>>(args...);
}
template <typename Kernel, typename... T>
void launch_kernel (
Kernel K,
max_jobs m,
T ...args
)
/*!
ensures
- This function is just like launch_kernel(K,args...) except that you can
additionally supply a max_jobs number that tells it how many possible
total threads could be used. This is useful when launching potentially
small jobs that might not need the number of threads suggested by
launch_kernel().
!*/
{
int num_blocks, num_threads;
CHECK_CUDA(cudaOccupancyMaxPotentialBlockSize(&num_blocks,&num_threads,K));
// Check if the job is really small and we don't really need to launch a kernel
// with this many blocks and threads.
if (num_blocks*num_threads > m.num)
num_blocks = (m.num+num_threads-1)/num_threads;
K<<<num_blocks,num_threads>>>(args...);
}
// ------------------------------------------------------------------------------------
class grid_stride_range
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment