Shared Memory and Synchronization in CUDA Programming
This article lets u know what is
shared memory and synchronization with detail and complete working example.
Let’s start our discussion. We start with this question;
What is
Shared Memory and Synchronization in CUDA Programming?
Motivation
The motivation for splitting blocks
into threads was simply one of working around hardware limitations to the
number of blocks we can have in flight. This is fairly weak motivation, because
this could easily be done behind the scenes by the CUDA runtime. Fortunately,
there are other reasons one might want to split a block into threads.
Shared
Memory in CUDA
CUDA C
makes available a region of memory that we call shared memory. This region of
memory brings along with it another extension to the C language akin to __device__ and __global__. As a
programmer, we can modify our variable declarations with the CUDA C keyword __shared__ to make this variable
resident in shared memory. But what’s
the point?
We’re
glad you asked.
The CUDA C compiler treats variables in shared memory differently than typical
variables. It creates a copy of the variable for each block that you
launch on the GPU. Every thread in that block shares the memory, but threads
cannot see or modify the copy of this variable that is seen within other
blocks. This provides an excellent means by which threads within a block can
communicate and collaborate on computations. Furthermore, shared memory buffers
reside physically on the GPU as opposed to residing in off-chip DRAM. Because
of this, the latency to access shared memory tends to be far lower than typical
buffers, making shared memory effective as a per-block, software managed cache
or scratchpad.
Fig shows you the memory hierarchy
diagram in CUDA Arch. With Shared Memory
Motivation
of Synchronization
Race
Condition
The prospect of communication between
threads should excite you. It excites me, too. But nothing in life is free, and
interthread communication is no exception. If
we expect to communicate between threads, we also need a mechanism for
synchronizing between threads. For example, if thread A writes a value to
shared memory and we want thread B to do something with this value, we can’t
have thread B start its work until we know the write from thread A is complete. Without synchronization, we have created
a race condition where the correctness of the execution results depends on the
nondeterministic details of the hardware.
Shared
Memory and Global Memory
Shared
memory
Threads within the same block have
two main ways to communicate data with each other. The fastest way would be to
use shared memory. When a block of threads starts executing, it runs on an SM,
a multiprocessor unit inside the GPU. Each
SM has a fairly small amount of shared memory associated with it, usually 16KB
of memory. To make matters more difficult, often times, multiple thread
blocks can run simultaneously on the same SM.
For example, if
each SM has 16KB of shared memory and there are 4 thread blocks running
simultaneously on an SM, then the maximum amount of shared memory available to
each thread block would be 16KB/4, or 4KB. So as you can see, if you only need
the threads to share a small amount of data at any given time, using shared
memory is by far the fastest and most convenient way to do it.
Global
memory
However, if your program is using
too much shared memory to store data, or your threads simply need to share too
much data at once, then it is possible that the shared memory is not big enough
to accommodate all the data that needs to be shared among the threads. In such
a situation, threads always have the option of writing to and reading from
global memory. Global memory is much slower than accessing shared memory;
however, global memory is much larger.
For most video cards sold today, there is at least 128MB of memory the GPU can
access.
Looking for the example?
Declaring
shared arrays
For CUDA kernels, there is a special
keyword, __shared__, which places a
variable into shared memory for each respective thread block. The __shared__ keyword works on any type of
variable or array. In the case for this tutorial, we will be declaring three
arrays in shared memory.
// Declare arrays to
be in shared memory.
// 256 elements * (4
bytes / element) * 3 = 3KB.
__shared__ float min[256];
__shared__ float max[256];
__shared__ float avg[256];
|
If you are not clear with idea of thread and block architecture and how to decide. please go through this link
For descriptive example; Vector Dot Product
For Simple example with more description; Simple and explained
For Simple example with more description; Simple and explained
Summary
of the Article
In summing up this article,
it is possible, and many times necessary, for threads within the same block to
communicate with each other through either shared memory, or global memory.
Shared memory is by far the fastest way, however due to it’s size limitations,
some problems will be forced to use global memory for thread communication.
Using __syncthreads is sometimes
necessary to ensure that all data from all threads is valid before threads read
from shared memory which is written to by other threads. Below is a graph of
execution time it took my CPU against the amount of time it took my graphics
card. the CPU is a 2.66 Core 2 Duo, while the graphics card is a GTX 280,
slightly underclocked. As you can
see, the GPU is faster when there are at least a million elements, and the
spread between the GPU and CPU continues to widen with more elements. However,
main system memory may be a significant bottleneck which is preventing the GPU
from achieving more than 1.5x the processor performance.
Got Questions?
Feel free to ask me any
question because I'd be happy to walk you through step by step!
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Posts
guys the program that i'm working on is for matrix multiplication using non shared and shared memory, and it shows the same calculation time, if possible could you have a look at the code below thanks.
ReplyDelete#include
#include
#include
# define TILE_WIDTH 32
__global__ void gpu_matrixmult (int *Md, int *Nd, int *Pd, int Width)
{
__shared__ int Mds[TILE_WIDTH][TILE_WIDTH];
__shared__ int Nds[TILE_WIDTH][TILE_WIDTH];
int bx = blockIdx.x; // tile (block) indices
int by = blockIdx.y;
int tx = threadIdx.x; //thread indices
int ty = threadIdx.y;
int Row = by * TILE_WIDTH + ty; // global indices
int Col = bx * TILE_WIDTH + tx;
int Pvalue = 0;
for (int m = 0; m < Width/TILE_WIDTH; m++)
{
Mds[ty][tx] = Md[Row*Width + (m*TILE_WIDTH + tx)]; // load Md, Nd tiles into sh. mem
Nds[ty][tx] = Nd[(m*TILE_WIDTH + ty)*Width + Col];
__syncthreads();
for ( int k = 0; k < TILE_WIDTH; k++)
Pvalue += Mds[ty][k] * Nds[k][tx];
}
Pd[Row * Width + Col] = Pvalue;
}
int main ()
{
int *A, *B, *C;
int N=10;
int i,j; //loop counters
int size;
char key;
int* Ad;
int* Bd;
int* Cd;
cudaEvent_t start, stop; // using cuda events to measure time
float elapsed_time_ms; // which is applicable for asynchronous code also
//keyboard input
do
{
printf("Enter size of array in one dimension (square array), currently %d\n",N);
scanf("%d",&N);
dim3 Block(TILE_WIDTH, TILE_WIDTH);
dim3 Grid(N / TILE_WIDTH, N / TILE_WIDTH);
size = N* N * sizeof(int);
A = (int*) malloc(size);
B = (int*) malloc(size);
C = (int*) malloc(size);
for(i=0;i < N;i++) { // load arrays with some numbers
for(j=0;j < N;j++) {
A[i * N + j] = i;
B[i * N + j] = i;
}
}
cudaMalloc((void**)&Ad, size);
cudaMalloc((void**)&Bd, size);
cudaMalloc((void**)&Cd, size);
cudaMemcpy(Ad, A, size, cudaMemcpyHostToDevice);
cudaMemcpy(Bd, B, size, cudaMemcpyHostToDevice);
cudaMemcpy(Cd, C, size, cudaMemcpyDeviceToHost);
cudaEventCreate(&start); // instrument code to measure start time
cudaEventCreate(&stop);
cudaEventRecord(start, 0); // here start time, after memcpy
// Launch the device computation
gpu_matrixmult<<>>(Ad, Bd, Cd, N);
// Read C from the device
cudaMemcpy(C, Cd, size, cudaMemcpyDeviceToHost);
cudaEventRecord(stop, 0); // measuse end time
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsed_time_ms, start, stop );
printf("Time to calculate results on GPU: %f ms.\n", elapsed_time_ms);
printf("\nEnter c to repeat, return to terminate\n");
scanf("%c",&key);
scanf("%c",&key);
} while (key == 'c');
// Free device memory
cudaFree(Ad);
cudaFree(Bd);
cudaFree(Cd);
cudaEventDestroy(start);
cudaEventDestroy(stop);
system ("pause");
}
Have you walk through this implementation
Delete"http://cuda-programming.blogspot.in/2013/01/cuda-c-program-for-matrix-addition-and.html"
And try to reduce you Tile width to some smaller number like ;
ReplyDelete16,8,4 or 2
Did you copy and paste this from section 5.3 of the "Cuda By Example" book?
ReplyDeleteOf course. This whole blogspot is all about copying from that book.
Deletefor a matrix vector multiplication can you please help me write the cuda device operations of block and threads
ReplyDeletefor a matrix vector multiplication can you please help me write the cuda device operations of block and threads
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