CUDA Streams
These notes will introduce the use of multiple CUDA
streams to overlap memory transfers with kernel
computations.
Also introduced is paged-locked memory
These materials come from Chapter 10 of “CUDA by Example” by Jason
Sanders and Edwards Kandrot.
ITCS 6/8010 CUDA Programming, UNC-Charlotte, B. Wilkinson, Feb 14, 2011
Streams.pptx
1
Page-locked host memory
(also called pinned host memory)
Page-locked memory is not paged in and out main
memory by the OS through paging but will remain
resident.
Allows:
•
•
•
Concurrent host/device memory transfers with kernel operations
(Compute capability 2.x) – see next
Host memory can be mapped to device address space
(Compute capability > 1.0)
Memory bandwidth is higher
• Uses real addresses rather than virtual addresses
• Does not need to intermediate copy buffering
2
Note on using page-locked memory
Using page-locked memory will reduce
memory available to the OS for paging and
so need to be careful in allocating it
3
Allocating page locked memory
cudaMallocHost ( void ** ptr,
size_t size )
Allocates page-locked host memory that is accessible to
device
cudaHostAlloc ( void ** ptr, size_t size, unsigned int flags)
Allocates page-locked host memory that is accessible to
device – seems to have more options
4
//Pinned memory test written by Barry Wilkinson, UNC-Charlotte. Feb 10, 2011.
Test of
Pinned
Memory
#include <stdio.h>
#include <cuda.h>
#include <stdlib.h>
#define SIZE (10*1024*1024) // number of bytes in arrays 10 MBytes
int main(int argc, char *argv[]) {
int i;
int *a;
int *dev_a;
// loop counter
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
// using cuda events to measure time
// create events
float elapsed_time_ms1, elapsed_time_ms3;
/* --------------------ENTER INPUT PARAMETERS AND DATA -----------------------*/
cudaMalloc((void**)&dev_a, SIZE);
// allocate memory on device
/* ---------------- COPY USING PINNED MEMORY -------------------- */
cudaHostAlloc((void**)&a, SIZE ,cudaHostAllocDefault);
// allocate page-locked memory on host
cudaEventRecord(start, 0);
for(i = 0; i < 100; i++) {
cudaMemcpy(dev_a, a , SIZE ,cudaMemcpyHostToDevice);
//copy to device
cudaMemcpy(a,dev_a, SIZE ,cudaMemcpyDeviceToHost);
//copy back to host
}
cudaEventRecord(stop, 0);
// instrument code to measue end time
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsed_time_ms1, start, stop );
printf("Time to copy %d bytes of data 100 times on GPU, pinned memory: %f ms\n", SIZE, elapsed_time_ms1); // exec. time
5
/* ---------------- COPY USING REGULAR MEMORY-------------------- */
a = (int*) malloc(SIZE); // allocate regular memory on host
cudaEventRecord(start, 0);
for(i = 0; i < 100; i++) {
cudaMemcpy(dev_a, a , SIZE ,cudaMemcpyHostToDevice); //copy to device
cudaMemcpy(a,dev_a, SIZE ,cudaMemcpyDeviceToHost);
//copy back to host
}
cudaEventRecord(stop, 0);
// instrument code to measue end time
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsed_time_ms3, start, stop );
printf("Time to copy %d bytes of data 100 times on GPU: %f ms\n", SIZE, elapsed_time_ms3); // exec. time
/*--------------------------SPEEDUP ---------------------------------*/
printf("Speedup of using pinned memory = %f\n", (float) elapsed_time_ms3 / (float) elapsed_time_ms1);
/* -------------- clean up ---------------------------------------*/
free(a);
cudaFree(dev_a);
cudaEventDestroy(start);
cudaEventDestroy(stop);
return 0;
}
6
My code
7
Using NVIDIA sample code for bandwidth on
coit-grid06
./bandwidthTest Starting...
Running on...
Device 0: Tesla C2050
Quick Mode
Host to Device Bandwidth, 1 Device(s), Paged memory
Transfer Size (Bytes)
Bandwidth(MB/s)
33554432
1026.7
Device to Host Bandwidth, 1 Device(s), Paged memory
Transfer Size (Bytes)
Bandwidth(MB/s)
33554432
1108.1
Device to Device Bandwidth, 1 Device(s)
Transfer Size (Bytes)
Bandwidth(MB/s)
33554432
84097.6
[bandwidthTest] - Test results:
PASSED
Press <Enter> to Quit...
-----------------------------------------------------------
8
CUDA Streams
A CUDA Stream is a sequence of operations
(commands) that are executed in order.
CUDA streams can be created and executed together
and interleaved although the “program order” is always
maintained within each stream.
Streams proved a mechanism to overlap memory
transfer and computations operations in different
stream for increased performance if sufficient
resources are available.
9
Creating a stream
Done by creating a stream object and associated it
with a series of CUDA commands that then becomes
the stream. CUDA commands have a stream pointer
as an argument:
Cannot use
cudaStream_t stream1;
cudaStreamCreate(&stream1);
cudaMemcpyAsync(…, stream1);
MyKernel<<< grid, block, stream1>>>(…);
cudaMemcpyAsync(… , stream1);
Stream
regular
cudaMemcpy
with streams,
need
asynchronous
commands for
concurrent
operation see
next
10
cudaMemcpyAsync( …, stream)
Asynchronous version of cudaMemcpy that copies
date to/from host and the device
May return before copy complete
A stream argument specified.
Needs “page-locked” memory
11
#define SIZE (N*20)
…
int main(void) {
int *a, *b, *c;
int *dev_a, *dev_b, *dev_c;
Code Example
Page 194-95 CUDA by
Example, without error
detection macros
cudaMalloc( (void**)&dev_a, N * sizeof(int) );
cudaMalloc( (void**)&dev_b, N * sizeof(int) );
cudaMalloc( (void**)&dev_c, N * sizeof(int) );
One stream
cudaHostAlloc((void**)&a,SIZE*sizeof(int),cudaHostAllocDefault); // paged-locked
cudaHostAlloc((void**)&b,SIZE*sizeof(int),cudaHostAllocDefault);
cudaHostAlloc((void**)&c,SIZE*sizeof(int),cudaHostAllocDefault);
for(int i=0;i<SIZE;i++) {
a[i] = rand();
b[i] = rand();
}
}
// load data
for(int i=0;I < SIZE;i+= N {
// loop over data in chunks
cudaMemcpyAsync(dev_a,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream);
cudaMemcpyAsync(dev_b,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream);
kernel<<<N/256,256,0,stream>>>(dev_a,dev-b,dev_c);
cudaMemcpyAsync(c+1,dev_c,N*sizeof(int),cudaMemcpyDeviceToHost,stream)
}
cudaStreamSynchronise(stream); // wait for stream to finish
return 0;
12
Multiple streams
Assuming device can support it (can check in code if
needed), create two streams with:
cudaStream_t stream1, stream2;
cudaStreamCreate(&stream1);
cudaStreamCreate(&stream2);
and then duplicate stream code for each stream
13
int *dev_a1, *dev_b1, *dev_c1; // stream 1 mem ptrs
int *dev_a2, *dev_b2, *dev_c2; // stream 2 mem ptrs
First attempt
//stream 1
described in book
cudaMalloc( (void**)&dev_a1, N * sizeof(int) );
cudaMalloc( (void**)&dev_b1, N * sizeof(int) );
concatenate
cudaMalloc( (void**)&dev_c1, N * sizeof(int) );
statements of each
//stream 2
cudaMalloc( (void**)&dev_a2, N * sizeof(int) );
stream
cudaMalloc( (void**)&dev_b2, N * sizeof(int) );
cudaMalloc( (void**)&dev_c2, N * sizeof(int) );
…
for(int i=0;I < SIZE;i+= N*2 {
// loop over data in chunks
// stream 1
cudaMemcpyAsync(dev_a1,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream1);
cudaMemcpyAsync(dev_b1,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream1);
kernel<<<N/256,256,0,stream1>>>(dev_a,dev-b,dev_c);
cudaMemcpyAsync(c+1,dev_c1,N*sizeof(int),cudaMemcpyDeviceToHost,stream1)
//stream 2
cudaMemcpyAsync(dev_a2,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream2);
cudaMemcpyAsync(dev_b2,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream2);
kernel<<<N/256,256,0,stream2>>>(dev_a,dev-b,dev_c);
cudaMemcpyAsync(c+1,dev_c2,N*sizeof(int),cudaMemcpyDeviceToHost,stream2)
}
cudaStreamSynchronise(stream1); // wait for stream to finish
cudaStreamSynchronise(stream2); // wait for stream to finish
14
Simply
concatenating
statements
does not work
well because of
the way the
GPU
schedules work
15
Page 206 CUDA by Example,
16
Page 207 CUDA by Example,
17
Page 208 CUDA by Example
Second attempt described in book
Interleave statements of each stream
for(int i=0;I < SIZE;i+= N*2 {
// loop over data in chunks
// interleave stream 1 and stream 2
cudaMemcpyAsync(dev_a1,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream1);
cudaMemcpyAsync(dev_a2,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream2);
cudaMemcpyAsync(dev_b1,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream1);
cudaMemcpyAsync(dev_b2,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream2);
kernel<<<N/256,256,0,stream1>>>(dev_a,dev-b,dev_c);
kernel<<<N/256,256,0,stream2>>>(dev_a,dev-b,dev_c);
cudaMemcpyAsync(c+1,dev_c1,N*sizeof(int),cudaMemcpyDeviceToHost,stream1)
cudaMemcpyAsync(c+1,dev_c2,N*sizeof(int),cudaMemcpyDeviceToHost,stream2)
}
18
Page 210 CUDA by Example
19
Questions