Using the shuffle command, are there race conditions/lost updates when two different threads concurrently attempt to update the same register value?
This is a late answer provided here to remove this question from the unanswered list.
From the CUDA C Programming Guide
The __shfl() intrinsics permit exchanging of a variable between threads within
a warp without use of shared memory
The idea is that a thread i can read, but not alter, the value of a register r assigned to thread j. So, and as pointed out in the comments above, there is no race condition.
The CUDA C Programming Guide provides also the following example to broadcast of a single value across a warp
global__ void bcast(int arg) {
int laneId = threadIdx.x & 0x1f;
int value;
if (laneId == 0) // Note unused variable for
value = arg; // all threads except lane 0
value = __shfl(value, 0); // Get "value" from lane 0
if (value != arg) printf("Thread %d failed.\n", threadIdx.x); }
void main() {
bcast<<< 1, 32 >>>(1234);
cudaDeviceSynchronize();
}
In this example, the value of the value register assigned to thread 0 in the warp is broadcast to all other threads in the warp and assigned to the local value registers. All the other threads are not attempting (but also cannot) alter the value of the value register assigned to thread 0.
Related
we recently encountered some CUDA memory model related issues when doing cross-CTA communication.
We are seeking an authoritative answer from CUDA memory model experts.
Specifically, we want to know whether causality order remain transitive across different scopes.
The specific case is as follows:
__device__ unsigned int count = 0;
__shared__ bool isLastBlockDone;
__global__ void sum(const float* array, unsigned int N,
float* result)
{
// Each block sums a subset of the input array.
float partialSum = calculatePartialSum(array, N);
if (threadIdx.x == 0) {
// Thread 0 of each block stores the partial sum
// to global memory.
result[blockIdx.x] = partialSum;
// Thread 0 makes sure that the incrementation
// of the "count" variable is only performed after
// the partial sum has been written to global memory.
__threadfence();
// Thread 0 signals that it is done.
unsigned int value = atomicInc(&count, gridDim.x);
// Thread 0 determines if its block is the last
// block to be done.
isLastBlockDone = (value == (gridDim.x - 1));
}
// Synchronize to make sure that each thread reads
// the correct value of isLastBlockDone.
__syncthreads();
if (isLastBlockDone) {
// The last block sums the partial sums
// stored in result[0 .. gridDim.x-1]
float totalSum = calculateTotalSum(result);
if (threadIdx.x == 0) {
// Thread 0 of last block stores the total sum
// to global memory and resets the count
// varialble, so that the next kernel call
// works properly.
result[0] = totalSum;
count = 0;
}
}
}
In the above case, the result array is not declared as volatile, so they may be cached incoherently in L1.
So we want to know, according to CUDA memory model, when the last block executes calculateTotalSum in line 36, will it read out other CTA’s partial sum safely?
We suspect this has something to do with causality order transitivity across different scopes:
With threadfence(line 18) and atomic operations(line 21), causality order is established in gpu scope between other block's write to result array(line 13)and last block’s write to the isLastBlockDone flag(line 25):
write_result_array -> write_isLastBlockDone.
With __syncthreads(line 30), causality order is established in cta scope between write isLastBlockDone(line 25) and read result array(line 36):
write_isLastBlockDone -> read_result_array.
Can the causality order maintain transitive across different scopes according to the cuda memory model?
For example, in our case, does the following causality order hold?:
write_result_array -> write_isLastBlockDone -> read_result_array.
Is this code correct according to cuda memory model?
Does causality order remain transitive across different scopes?
Let's suppose we want to call a global function with the code that follows. Every single thread will have a curandState generator and an array of ints (both properly initialized) that we'll use in order to execute the following code sample:
#define NUMTHREADS 200
int main(){
int * result;
curandState * randState;
if (cudaMalloc(&randState, NUMTHREADS * sizeof(curandState)) == cudaErrorMemoryAllocation ||
cudaMalloc(&result, NUMTHREADS * sizeof(int)) == cudaErrorMemoryAllocation){
cudaDeviceReset();
exit(-1);
}
setup_cuRand <<<1, NUMTHREADS>>> (randState, unsigned(time(NULL)));
method <<<1, NUMTHREADS>>> (state,result);
return 1;
}
__global__ void setup_cuRand(curandState * state, unsigned long seed)
{
int id = threadIdx.x;
curand_init(seed, id, 0, &state[id]);
}
__global__ void generic method(curandState* state, int * result){
curandState localState = state[threadIdx.x];
int num = curand(&localState) % 100;
if(num > 50)
result[threadIdx.x] = threadIdx.x;
else
result[threadIdx.x] = -1;
}
What would be our execution? I mean, do the threads split into both codes magically and re-join later or how it works? are all 1024 threads in execution at once? this last question is because when i'm debugging on Visual Studio 2013, using Cuda Debugger, when i'm going forward, threadIdx.x allways has a value like n*32 and until now i tought that 1024 threads could be executed at the same time and now i'm doubtfull
The test is likely to be transformed into a predicate that will mean conditional assignment of some value in your region of memory. Should your if be more complex, the threads of a warp would magically join after the second part of the if clause. Depending on predicate for each thread of a warp, a branch might not even get visited.
When entering a breakpoint, the data will be shown for a specific thread/block id. Which thread/block is followed is given by the CUDA Debug Focus setting in NSIGHT for Visual Studio (While debugging with CUDA, enter the NSIGHT menu entry, and select Windows, then CUDA Debug Focus...) By default, thread 0,0,0 will be focused.
Threads are logically executed at the same time, but in practice, you have less than 1024 CUDA-cores per SM. The threads are organized into warps of 32, and warps are scheduled on different execution units by the instruction scheduler.
For 1024 threads, that is 32 warps, the first and last warp are not necessarily executed at the same time precisely.
See Memory Fence function in cuda documentation for more details, as well as Synchronization Functions.
I understand the purpose of __syncthreads(), but I sometimes find it overused in some codes.
For instance, in the code below taken from NVIDIA notes, each thread calculates mainly s_data[tx]-s_data[tx-1]. Each thread needs the data it reads from the global memory and the data read by its neighboring thread. Both threads will be in the same warp and hence should complete retrieval of their data from the global memory and are scheduled for execution simultaneously.
I believe the code will still work without __syncthread(), but obviously the NVIDIA notes say otherwise. Any comment, please?
// Example – shared variables
// optimized version of adjacent difference
__global__ void adj_diff(int *result, int *input)
{
// shorthand for threadIdx.x
int tx = threadIdx.x;
// allocate a __shared__ array, one element per thread
__shared__ int s_data[BLOCK_SIZE];
// each thread reads one element to s_data
unsigned int i = blockDim.x * blockIdx.x + tx;
s_data[tx] = input[i];
// avoid race condition: ensure all loads
// complete before continuing
__syncthreads();
if(tx > 0)
result[i] = s_data[tx] – s_data[tx–1];
else if(i > 0)
{
// handle thread block boundary
result[i] = s_data[tx] – input[i-1];
}
}
It would be nice if you included a link to where, in the "Nvidia notes", this appeared.
both threads will be in the same warp
No, they won't, at least not in all cases. What happens when tx = 32? Then the thread corresponding to tx belongs to warp 1 in the block, and the thread corresponding to tx-1 belongs to warp 0 in the block.
There's no guarantee that warp 0 has executed before warp 1, so the code could fail without the call to __synchtreads() (since, without it, the value of s_data[tx-1] could be invalid, since warp 0 hasn't run and therefore hasn't loaded it yet.)
A number of algorithms iterate until a certain convergence criterion is reached (e.g. stability of a particular matrix). In many cases, one CUDA kernel must be launched per iteration. My question is: how then does one efficiently and accurately determine whether a matrix has changed over the course of the last kernel call? Here are three possibilities which seem equally unsatisfying:
Writing a global flag each time the matrix is modified inside the kernel. This works, but is highly inefficient and is not technically thread safe.
Using atomic operations to do the same as above. Again, this seems inefficient since in the worst case scenario one global write per thread occurs.
Using a reduction kernel to compute some parameter of the matrix (e.g. sum, mean, variance). This might be faster in some cases, but still seems like overkill. Also, it is possible to dream up cases where a matrix has changed but the sum/mean/variance haven't (e.g. two elements are swapped).
Is there any of the three options above, or an alternative, that is considered best practice and/or is generally more efficient?
I'll also go back to the answer I would have posted in 2012 but for a browser crash.
The basic idea is that you can use warp voting instructions to perform a simple, cheap reduction and then use zero or one atomic operations per block to update a pinned, mapped flag that the host can read after each kernel launch. Using a mapped flag eliminates the need for an explicit device to host transfer after each kernel launch.
This requires one word of shared memory per warp in the kernel, which is a small overhead, and some templating tricks can allow for loop unrolling if you provide the number of warps per block as a template parameter.
A complete working examplate (with C++ host code, I don't have access to a working PyCUDA installation at the moment) looks like this:
#include <cstdlib>
#include <vector>
#include <algorithm>
#include <assert.h>
__device__ unsigned int process(int & val)
{
return (++val < 10);
}
template<int nwarps>
__global__ void kernel(int *inout, unsigned int *kchanged)
{
__shared__ int wchanged[nwarps];
unsigned int laneid = threadIdx.x % warpSize;
unsigned int warpid = threadIdx.x / warpSize;
// Do calculations then check for change/convergence
// and set tchanged to be !=0 if required
int idx = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int tchanged = process(inout[idx]);
// Simple blockwise reduction using voting primitives
// increments kchanged is any thread in the block
// returned tchanged != 0
tchanged = __any(tchanged != 0);
if (laneid == 0) {
wchanged[warpid] = tchanged;
}
__syncthreads();
if (threadIdx.x == 0) {
int bchanged = 0;
#pragma unroll
for(int i=0; i<nwarps; i++) {
bchanged |= wchanged[i];
}
if (bchanged) {
atomicAdd(kchanged, 1);
}
}
}
int main(void)
{
const int N = 2048;
const int min = 5, max = 15;
std::vector<int> data(N);
for(int i=0; i<N; i++) {
data[i] = min + (std::rand() % (int)(max - min + 1));
}
int* _data;
size_t datasz = sizeof(int) * (size_t)N;
cudaMalloc<int>(&_data, datasz);
cudaMemcpy(_data, &data[0], datasz, cudaMemcpyHostToDevice);
unsigned int *kchanged, *_kchanged;
cudaHostAlloc((void **)&kchanged, sizeof(unsigned int), cudaHostAllocMapped);
cudaHostGetDevicePointer((void **)&_kchanged, kchanged, 0);
const int nwarps = 4;
dim3 blcksz(32*nwarps), grdsz(16);
// Loop while the kernel signals it needs to run again
do {
*kchanged = 0;
kernel<nwarps><<<grdsz, blcksz>>>(_data, _kchanged);
cudaDeviceSynchronize();
} while (*kchanged != 0);
cudaMemcpy(&data[0], _data, datasz, cudaMemcpyDeviceToHost);
cudaDeviceReset();
int minval = *std::min_element(data.begin(), data.end());
assert(minval == 10);
return 0;
}
Here, kchanged is the flag the kernel uses to signal it needs to run again to the host. The kernel runs until each entry in the input has been incremented to above a threshold value. At the end of each threads processing, it participates in a warp vote, after which one thread from each warp loads the vote result to shared memory. One thread reduces the warp result and then atomically updates the kchanged value. The host thread waits until the device is finished, and can then directly read the result from the mapped host variable.
You should be able to adapt this to whatever your application requires
I'll go back to my original suggestion. I've updated the related question with an answer of my own, which I believe is correct.
create a flag in global memory:
__device__ int flag;
at each iteration,
initialize the flag to zero (in host code):
int init_val = 0;
cudaMemcpyToSymbol(flag, &init_val, sizeof(int));
In your kernel device code, modify the flag to 1 if a change is made to the matrix:
__global void iter_kernel(float *matrix){
...
if (new_val[i] != matrix[i]){
matrix[i] = new_val[i];
flag = 1;}
...
}
after calling the kernel, at the end of the iteration (in host code), test for modification:
int modified = 0;
cudaMemcpyFromSymbol(&modified, flag, sizeof(int));
if (modified){
...
}
Even if multiple threads in separate blocks or even separate grids, are writing the flag value, as long as the only thing they do is write the same value (i.e. 1 in this case), there is no hazard. The write will not get "lost" and no spurious values will show up in the flag variable.
Testing float or double quantities for equality in this fashion is questionable, but that doesn't seem to be the point of your question. If you have a preferred method to declare "modification" use that instead (such as testing for equality within a tolerance, perhaps).
Some obvious enhancements to this method would be to create one (local) flag variable per thread, and have each thread update the global flag variable once per kernel, rather than on every modification. This would result in at most one global write per thread per kernel. Another approach would be to keep one flag variable per block in shared memory, and have all threads simply update that variable. At the completion of the block, one write is made to global memory (if necessary) to update the global flag. We don't need to resort to complicated reductions in this case, because there is only one boolean result for the entire kernel, and we can tolerate multiple threads writing to either a shared or global variable, as long as all threads are writing the same value.
I can't see any reason to use atomics, or how it would benefit anything.
A reduction kernel seems like overkill, at least compared to one of the optimized approaches (e.g. a shared flag per block). And it would have the drawbacks you mention, such as the fact that anything less than a CRC or similarly complicated computation might alias two different matrix results as "the same".
How can I write a statement in my CUDA kernel that is executed by a single thread. For example if I have the following kernel:
__global__ void Kernel(bool *d_over, bool *d_update_flag_threads, int no_nodes)
{
int tid = blockIdx.x*blockDim.x + threadIdx.x;
if( tid<no_nodes && d_update_flag_threads[tid])
{
...
*d_over=true; // writing a single memory location, only 1 thread should do?
...
}
}
In above kernel, "d_over" is a single boolean flag while "d_update_flag_threads" is a boolean array.
What I normally did before is using the first thread in the thread block e.g.:
if(threadIdx.x==0)
but It could not work in this case as I have a flag array here and only threads with assosiated flag "true" will execute the if statement. That flag array is set by another CUDA kernel called before and I don't have any knowledge about it in advance.
In short, I need something similar to "Single" construct in OpenMP.
A possible approach is use atomic operations. If you need only one thread per block to do the update, you could do the atomic operation in shared memory (for compute capability >= 1.2) which is generally much faster than perform it in global memory.
Said that, the idea is as follow:
int tid = blockIdx.x*blockDim.x + threadIdx.x;
__shared__ int sFlag;
// initialize flag
if (threadIdx.x == 0) sFlag = 0;
__syncthreads();
if( tid<no_nodes && d_update_flag_threads[tid])
{
// safely update the flag
int singleFlag = atomicAdd(&sFlag, 1);
// custom single operation
if ( singleFlag == 0)
*d_over=true; // writing a single memory location, only 1 thread will do it
...
}
It is just an idea. I've not tested it but is close to an operation performed by a single thread, not being the first thread of the block.
You could use atomicCAS(d_over, 0, 1) where d_over is declared or type-cast as int*.
This would ensure that only the first thread that sees the d_over value to be 0 (false) would update it and nobody else would.