Is it possible to write data in a output file in parallel - parallel-processing

Is it possible to write data on a single output file with more processors. I mean consider some processors have a part of a data (e.g. a matrix) and the whole matrix should be written in a single output file. Is it possible each processors write own parts in parallel (at the same time not one after another)?

Yes it is absolutely possible, and MPI gives you all the tools to do so.
A great introduction to MPI I/O was already linked in the comments. I'm just going to a minimal example to demonstrate it;
#include <stdint.h>
#include <mpi.h>
#include <assert.h>
#include <stdlib.h>
#include <stdio.h>
const int N = 1024ll * 1024 * 256 * 12;
const MPI_Datatype MPI_T = MPI_UINT64_T;
typedef uint64_t T;
const char* filename = "mpi.out";
int main() {
MPI_Init(NULL, NULL);
int rank, size;
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
MPI_Comm_size(MPI_COMM_WORLD, &size);
assert(N % size == 0);
T* my_part = calloc(N / size, sizeof(T));
for (size_t i = 0; i < N / size; i++)
my_part[i] = i + rank * (N / size);
MPI_File fh;
MPI_File_open(MPI_COMM_WORLD, filename,
MPI_MODE_WRONLY | MPI_MODE_CREATE, MPI_INFO_NULL,
&fh);
MPI_File_set_view(fh, rank * (N / size) * sizeof(T), MPI_T, MPI_T,
"native", MPI_INFO_NULL);
MPI_Barrier(MPI_COMM_WORLD);
double begin = MPI_Wtime();
MPI_File_write_all(fh, my_part, N / size, MPI_T, MPI_STATUS_IGNORE);
MPI_Barrier(MPI_COMM_WORLD);
double duration = MPI_Wtime() - begin;
if (rank == 0)
printf("Wrote %llu B in %f s, %f GiB/s\n",
N * sizeof(T), duration,
N * sizeof(T) / (duration * 1024 * 1024 * 1024));
MPI_File_close(&fh);
MPI_Finalize();
}
With very minimal tuning (setting striping size to 12) and 12 ranks, this gets quite reasonable performance on Lustre of ~7.3 GiB/s. Note that this exceeds the raw throughput of a 4xFDR InfiniBand used by the system.
Typically you would use custom data types with such file views, or even more high-level I/O libraries such as HDF5 that work on top of MPI I/O. Getting optimal performance will probably require some site-specific tuning.
In practice, this is sufficient to know, but referring to the discussion on the other answer by user3666197 a few more nitpicky details:
This code snippet expresses concurrent file writes at a quite high-level of abstraction. You get truly parallel I/O by executing this code on an HPC system with a parallel file system. It is absolutely possible that, in a well-tuned configuration, that the bytes written from the different ranks follow entirely different paths and end up on different disks in different storage servers - all in parallel. What matters in your code is to express the concurrency - then you apply tuning to make sure it performs well - which means to allow the storage system to execute it efficiently and in parallel.

Q : Is it possible each processors write own parts in parallel (at the same time not one after another)?
No, it is not. Writing is a process of putting atomic-pieces-of information ( bits to tape, characters to a file-abstraction ) in a pure-[SERIAL] fashion.
If in doubts, take and hold 5 pencils in your hand ( I cannot make it, so it fine to just imagine that one can ) and try to write one word on a paper ( due to "process"-of-writing related circumstances - a singularity of how we write ), there is impossible to "write" 5-independent i.e. different words in this simplified example.
Similarly, in other form of illustration - if you have a typewriter machine ( hope it is not so archaic imagination ) - one can get 5-copies of the same pure-[SERIAL] sequence-of-characters ( thanks to the use of 4-pieces of carbon-copy papers filled between those 5-sheets of office paper ), yet none of these copies will differ from the original - so these are not independent ( like these would be in true-[PARALLEL] processes ) but just a set of replicas, which is productive use of time and resources in producing some paperwork for sending 1-original + 4-copies in some administrative Matrix, but not an example of true-[PARALLEL] writes.
Last but not least, any attempt to use more fingers at once than the one and only the one, for typing on a typewriter ( which puts a pure-[SERIAL] sequence of chars printed on paper ) will produce a mechanical jam, as the process of mechanical type-writing relies on a singular-point, where a character is printed, through a hit of an ink-banner, onto a paper.
Modern filesystems are far from this trivial archetype, yet have a similar concept of producing and maintaining a pure-[SERIAL] representation of a sequence of characters. Even while it is possible to open more filehandles having access "into" this sequence-of-chars, these do not mean one has a chance to make the file-I/O operations de-serialised, the less to take at-once ( as disk-heads are not present at several different locations of the magnetic-disk storage ( the less for tape-device ) at-once and neither the almost-random-access devices, like SSD et al, do not go this wild way, where they lose the control of low-level properties ( wear-leveling, elevator-optimisations, power-limiting and similar low-level device tricks ).

Related

CUDA Parallel Cross Product

Disclaimer: I am fairly new to CUDA and parallel programming - so if you're not going to bother to answer my question, just ignore this, or at least point me to the right resources so I can find the answer myself.
Here's the particular problem I'm looking to solve using parallel programming. I have some 1D arrays that store 3D vectors in this format -> [v0x, v0y, v0z, ... vnx, vny, vnz], where n is the vector, and x, y, z are the respective components.
Suppose I want to find the cross product between vectors [v0, v1, ... vn] in one array and their corresponding vectors [v0, v1, ... vn] in another array.
The calculation is pretty straightforward without parallelization:
result[x] = vec1[y]*vec2[z] - vec1[z]*vec2[y];
result[y] = vec1[z]*vec2[x] - vec1[x]*vec2[z];
result[z] = vec1[x]*vec2[y] - vec1[y]*vec2[x];
The problem I'm having is understanding how to implement CUDA parallelization for the arrays I currently have. Since each value in the result vector is a separate calculation, I can effectively run the above calculation for each vector in parallel. Since each component of the resulting cross product is a separate calculation, those too could run in parallel. How would I go about setting up the blocks and threads/ go about thinking about setting up the threads for such a problem?
The top 2 optimization priorities for any CUDA programmer are to use memory efficiently, and expose enough parallelism to hide latency. We'll use those to guide our algorithmic choices.
A very simple thread strategy (the thread strategy answers the question, "what will each thread do or be responsible for?") in any transformation (as opposed to reduction) type problem is to have each thread be responsible for 1 output value. Your problem fits the description of transformation - the output data set size is on the order of the input data set size(s).
I'll assume that you intended to have two equal length vectors containing your 3D vectors, and that you want to take the cross product of the first 3D vectors in each and the 2nd 3D vectors in each, and so on.
If we choose a thread strategy of 1 output point per thread (i.e. result[x] or result[y] or result[z], all together would be 3 output points), then we will need 3 threads to compute the output of each vector cross product. If we have enough vectors to multiply, then we will have enough threads to keep our machine "busy" and do a good job of hiding latency. As a rule of thumb, your problem will start to become interesting on GPUs if the number of threads is 10000 or more, so this means we would want your 1D vectors to consist of about 3000 3D vectors or more. Let's assume that is the case.
In order to tackle the memory efficiency objective, our first task is to load your vector data from global memory. We will want this ideally to be coalesced, which roughly means adjacent threads access adjacent elements in memory. We'll want the output stores to be coalesced also, and our thread strategy of choosing one output point/one vector component per thread will work nicely to support that.
For efficient memory usage, we'd like to ideally load each item from global memory only once. Your algorithm naturally involves a small amount of data reuse. The data reuse is evident since the computation of result[y] depends on vec2[z] and the computation of result[x] also depends on vec2[z] to pick just one example. Therefore a typical strategy when there is data reuse is to load the data first into CUDA shared memory, and then allow the threads to perform their computations based on the data in shared memory. As we will see, this makes it fairly easy/convenient for us to arrange for coalesced loads from global memory, since the global data load arrangement is no longer tightly coupled to the threads or the usage of the data for computation.
The last challenge is to figure out an indexing pattern so that each thread will select the proper elements from shared memory to multiply together. If we look at your calculation pattern that you have depicted in your question, we see that the first load from vec1 follows an offset pattern of +1(modulo 3) from the index that the result is being computed for. So x->y, y->z, and z -> x. Likewise we see a +2(modulo 3) for the next load from vec2, another +2(modulo 3) pattern for the next load from vec1 and another +1(modulo 3) pattern for the final load from vec2.
If we combine all these ideas, we can then write a kernel that should have generally efficient characteristics:
$ cat t1003.cu
#include <stdio.h>
#define TV1 1
#define TV2 2
const size_t N = 4096; // number of 3D vectors
const int blksize = 192; // choose as multiple of 3 and 32, and less than 1024
typedef float mytype;
//pairwise vector cross product
template <typename T>
__global__ void vcp(const T * __restrict__ vec1, const T * __restrict__ vec2, T * __restrict__ res, const size_t n){
__shared__ T sv1[blksize];
__shared__ T sv2[blksize];
size_t idx = threadIdx.x+blockDim.x*blockIdx.x;
while (idx < 3*n){ // grid-stride loop
// load shared memory using coalesced pattern to global memory
sv1[threadIdx.x] = vec1[idx];
sv2[threadIdx.x] = vec2[idx];
// compute modulo/offset indexing for thread loads of shared data from vec1, vec2
int my_mod = threadIdx.x%3; // costly, but possibly hidden by global load latency
int off1 = my_mod+1;
if (off1 > 2) off1 -= 3;
int off2 = my_mod+2;
if (off2 > 2) off2 -= 3;
__syncthreads();
// each thread loads its computation elements from shared memory
T t1 = sv1[threadIdx.x-my_mod+off1];
T t2 = sv2[threadIdx.x-my_mod+off2];
T t3 = sv1[threadIdx.x-my_mod+off2];
T t4 = sv2[threadIdx.x-my_mod+off1];
// compute result, and store using coalesced pattern, to global memory
res[idx] = t1*t2-t3*t4;
idx += gridDim.x*blockDim.x;} // for grid-stride loop
}
int main(){
mytype *h_v1, *h_v2, *d_v1, *d_v2, *h_res, *d_res;
h_v1 = (mytype *)malloc(N*3*sizeof(mytype));
h_v2 = (mytype *)malloc(N*3*sizeof(mytype));
h_res = (mytype *)malloc(N*3*sizeof(mytype));
cudaMalloc(&d_v1, N*3*sizeof(mytype));
cudaMalloc(&d_v2, N*3*sizeof(mytype));
cudaMalloc(&d_res, N*3*sizeof(mytype));
for (int i = 0; i<N; i++){
h_v1[3*i] = TV1;
h_v1[3*i+1] = 0;
h_v1[3*i+2] = 0;
h_v2[3*i] = 0;
h_v2[3*i+1] = TV2;
h_v2[3*i+2] = 0;
h_res[3*i] = 0;
h_res[3*i+1] = 0;
h_res[3*i+2] = 0;}
cudaMemcpy(d_v1, h_v1, N*3*sizeof(mytype), cudaMemcpyHostToDevice);
cudaMemcpy(d_v2, h_v2, N*3*sizeof(mytype), cudaMemcpyHostToDevice);
vcp<<<(N*3+blksize-1)/blksize, blksize>>>(d_v1, d_v2, d_res, N);
cudaMemcpy(h_res, d_res, N*3*sizeof(mytype), cudaMemcpyDeviceToHost);
// verification
for (int i = 0; i < N; i++) if ((h_res[3*i] != 0) || (h_res[3*i+1] != 0) || (h_res[3*i+2] != TV1*TV2)) { printf("mismatch at %d, was: %f, %f, %f, should be: %f, %f, %f\n", i, h_res[3*i], h_res[3*i+1], h_res[3*i+2], (float)0, (float)0, (float)(TV1*TV2)); return -1;}
printf("%s\n", cudaGetErrorString(cudaGetLastError()));
return 0;
}
$ nvcc t1003.cu -o t1003
$ cuda-memcheck ./t1003
========= CUDA-MEMCHECK
no error
========= ERROR SUMMARY: 0 errors
$
Note that I've chosen to write the kernel using a grid-stride loop. This isn't terribly important to this discussion, and not that relevant for this problem, because I've chosen a grid size equal to the problem size (4096*3). However for much larger problem sizes, you might choose a smaller grid size than the overall problem size, for some possible small efficiency gain.
For such a simple problem as this, it's fairly easy to define "optimality". The optimal scenario would be however long it takes to load the input data (just once) and write the output data. If we consider a larger version of the test code above, changing N to 40960 (and making no other changes), then the total data read and written would be 40960*3*4*3 bytes. If we profile that code and then compare to bandwidthTest as a proxy for peak achievable memory bandwidth, we observe:
$ CUDA_VISIBLE_DEVICES="1" nvprof ./t1003
==27861== NVPROF is profiling process 27861, command: ./t1003
no error
==27861== Profiling application: ./t1003
==27861== Profiling result:
Type Time(%) Time Calls Avg Min Max Name
GPU activities: 65.97% 162.22us 2 81.109us 77.733us 84.485us [CUDA memcpy HtoD]
30.04% 73.860us 1 73.860us 73.860us 73.860us [CUDA memcpy DtoH]
4.00% 9.8240us 1 9.8240us 9.8240us 9.8240us void vcp<float>(float const *, float const *, float*, unsigned long)
API calls: 99.10% 249.79ms 3 83.263ms 6.8890us 249.52ms cudaMalloc
0.46% 1.1518ms 96 11.998us 374ns 454.09us cuDeviceGetAttribute
0.25% 640.18us 3 213.39us 186.99us 229.86us cudaMemcpy
0.10% 255.00us 1 255.00us 255.00us 255.00us cuDeviceTotalMem
0.05% 133.16us 1 133.16us 133.16us 133.16us cuDeviceGetName
0.03% 71.903us 1 71.903us 71.903us 71.903us cudaLaunchKernel
0.01% 15.156us 1 15.156us 15.156us 15.156us cuDeviceGetPCIBusId
0.00% 7.0920us 3 2.3640us 711ns 4.6520us cuDeviceGetCount
0.00% 2.7780us 2 1.3890us 612ns 2.1660us cuDeviceGet
0.00% 1.9670us 1 1.9670us 1.9670us 1.9670us cudaGetLastError
0.00% 361ns 1 361ns 361ns 361ns cudaGetErrorString
$ CUDA_VISIBLE_DEVICES="1" /usr/local/cuda/samples/bin/x86_64/linux/release/bandwidthTest
[CUDA Bandwidth Test] - Starting...
Running on...
Device 0: Tesla K20Xm
Quick Mode
Host to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 6375.8
Device to Host Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 6554.3
Device to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 171220.3
Result = PASS
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
$
The kernel takes 9.8240us to execute, and in that time loads or stores a total of 40960*3*4*3 bytes of data. Therefore the achieved memory bandwidth by the kernel is 40960*3*4*3/0.000009824 or 150 GB/s. The proxy measurement for peak achievable on this GPU is 171 GB/s, so this kernel achieves 88% of the optimal throughput. With more careful benchmarking to run the kernel twice in a row, the 2nd execution requires only 8.99us to execute. This brings the achieved bandwidth in this case up to 96% of peak achievable throughput.

GPU sorting vs CPU sorting

I made a very naive implementation of the mergesort algorithm, which i turned to work on CUDA with very minimal implementation changes, the algorith code follows:
//Merge for mergesort
__device__ void merge(int* aux,int* data,int l,int m,int r)
{
int i,j,k;
for(i=m+1;i>l;i--){
aux[i-1]=data[i-1];
}
//Copy in reverse order the second subarray
for(j=m;j<r;j++){
aux[r+m-j]=data[j+1];
}
//Merge
for(k=l;k<=r;k++){
if(aux[j]<aux[i] || i==(m+1))
data[k]=aux[j--];
else
data[k]=aux[i++];
}
}
//What this code do is performing a local merge
//of the array
__global__
void basic_merge(int* aux, int* data,int n)
{
int i = blockIdx.x*blockDim.x + threadIdx.x;
int tn = n / (blockDim.x*gridDim.x);
int l = i * tn;
int r = l + tn;
//printf("Thread %d: %d,%d: \n",i,l,r);
for(int i{1};i<=(tn/2)+1;i*=2)
for(int j{l+i};j<(r+1);j+=2*i)
{
merge(aux,data,j-i,j-1,j+i-1);
}
__syncthreads();
if(i==0){
//Complete the merge
do{
for(int i{tn};i<(n+1);i+=2*tn)
merge(aux,data,i-tn,i-1,i+tn-1);
tn*=2;
}while(tn<(n/2)+1);
}
}
The problem is that no matter how many threads i launch on my GTX 760, the sorting performance is always much much more worst than the same code on CPU running on 8 threads (My CPU have hardware support for up to 8 concurrent threads).
For example, sorting 150 million elements on CPU takes some hundred milliseconds, on GPU up to 10 minutes (even with 1024 threads per block)! Clearly i'm missing some important point here, can you please provide me with some comment? I strongly suspect the the problem is in the final merge operation performed by the first thread, at that point we have a certain amount of subarray (the exact amount depend on the number of threads) which are sorted and need to me merged, this is completed by just one thread (one tiny GPU thread).
I think i should use come kind of reduction here, so each thread perform in parallel further more merge, and the "Complete the merge" step just merge the last two sorted subarray..
I'm very new to CUDA.
EDIT (ADDENDUM):
Thanks for the link, I must admit I still need some time to learn better CUDA before taking full advantage of that material.. Anyway, I was able to rewrite the sorting function in order to take advantage as long as possible of multiple threads, my first implementation had a bottleneck in the last phase of the merge procedure, which was performed by only one multiprocessor.
Now after the first merge, I use each time up to (1/2)*(n/b) threads, where n is the amount of data to sort and b is the size of the chunk of data sorted by each threads.
The improvement in performance is surprising, using only 1024 threads it takes about ~10 seconds to sort 30 milion element.. Well, this is still a poor result unfortunately! The problem is in the threads syncronization, but first things first, let's see the code:
__global__
void basic_merge(int* aux, int* data,int n)
{
int k = blockIdx.x*blockDim.x + threadIdx.x;
int b = log2( ceil( (double)n / (blockDim.x*gridDim.x)) ) + 1;
b = pow( (float)2, b);
int l=k*b;
int r=min(l+b-1,n-1);
__syncthreads();
for(int m{1};m<=(r-l);m=2*m)
{
for(int i{l};i<=r;i+=2*m)
{
merge(aux,data,i,min(r,i+m-1),min(r,i+2*m-1));
}
}
__syncthreads();
do{
if(k<=(n/b)*.5)
{
l=2*k*b;
r=min(l+2*b-1,n-1);
merge(aux,data,l,min(r,l+b-1),r);
}else break;
__syncthreads();
b*=2;
}while((r+1)<n);
}
The function 'merge' is the same as before. Now the problem is that I'm using only 1024 threads instead of the 65000 and more I can run on my CUDA device, the problem is that __syncthreads does not work as sync primitive at grid level, but only at block level!
So i can syncronize up to 1024 threads,that is the amount of threads supported per block. Without a proper syncronization each thread mess up the data of the other, and the merging procedure does not work.
In order to boost the performance I need some kind of syncronization between all the threads in the grid, seems that no API exist for this purpose, and i read about a solution which involve multiple kernel launch from the host code, using the host as barrier for all the threads.
I have a certain plan on how to implement this tehcnique in my mergesort function, I will provide you with the code in the near future. Did you have any suggestion on your own?
Thanks
It looks like all the work is being done in __global __ memory. Each write takes a long time and each read takes a long time making the function slow. I think it would help to maybe first copy your data to __shared __ memory first and then do the work in there and then when the sorting is completed(for that block) copy the results back to global memory.
Global memory takes about 400 clock cycles (or about 100 if the data happens to be in L2 cache). Shared memory on the other hand only takes 1-3 clock cycles to write and read.
The above would help with performance a lot. Some other super minor things you can try are..
(1) remove the first __syncthreads(); It is not really doing anything because no data is being past in between warps at that point.
(2) Move the "int b = log2( ceil( (double)n / (blockDim.x*gridDim.x)) ) + 1; b = pow( (float)2, b);" outside the kernel and just pass in b instead. This is being calculated over and over when it really only needs to be calculated once.
I tried to follow along on your algorithm but was not able to. The variable names were hard to follow...or... your code is above my head and I cannot follow. =) Hope the above helps.

Generating threads based on a variable length array in cuda?

Fist, let me explain what I am implementing. The goal of my program is to generate all possible, non-distinct combinations of a given character set on a cuda enabled GPU. In order to parallelize the work, I am initializing each thread to a starting character.
For instance, consider the character set abcdefghijklmnopqrstuvwxyz. In this case, there will ideally be 26 threads: characterSet[threadIdx.x] = a for example (in practice, there would obviously be an offset to span the entire grid so that each thread has a unique identifier).
Here is my code thus far:
//Used to calculate grid dimensions
int* threads;
int* blocks;
int* tpb;
int charSetSize;
void calculate_grid_parameters(int length, int size, int* threads, int* blocks, int* tpb){
//Validate input
if(!threads || !blocks || ! tpb){
cout <<"An error has occured: Null pointer passed to function...\nPress enter to exit...";
getchar();
exit(1);
}
//Declarations
const int maxBlocks = 65535; //Does not change
int maxThreads = 512; //Limit in order to provide more portability
int dev = 0;
int maxCombinations;
cudaDeviceProp deviceProp;
//Query device
//cudaGetDeviceProperties(&deviceProp, dev);
//maxThreads = deviceProp.maxThreadsPerBlock;
//Determine total threads to spawn
//Length of password * size of character set
//Each thread will handle part of the total number of the combinations
if(length > 3) length = 3; //Max length is 3
maxCombinations = length * size;
assert(maxCombinations < (maxThreads * maxBlocks));
}
It is fairly basic.
I've limited length to 3 for a specific reason. The full character set, abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789 !\"#$&'()*+-.:;<>=?#[]^_{}~| is, I believe, 92 characters. This means for a length of 3, there are 778,688 possible non-distinct combinations. If it were length 4, than it would be roughly 71 million, and the maximum number of threads for my GPU is about 69 million (in one dimension). Furthermore, these combinations have already been generated in a file that will be read into an array and then delegated a specific initializing thread.
This leads me to my problem.
The maximum number of blocks on a cuda GPU (for 1-d) is 65,535. Each of those blocks (on my gpu) can run 1024 threads in one dimension. I've limited it to 512 in my code for portability purposes (this may be unnecessary). Ideally, each block should run 32 threads or a multiple of 32 threads in order to be efficient. The issue I have is how many threads I need. Like I said above, if I am using a full character set of length 3 for the starting values, this necessitates 778,688 threads. This happens to be divisible by 32, yielding 24,334 blocks assuming each block runs 32 threads. However, if I run the same character set with length two, I am left with 264.5 blocks each running 32 threads.
Basically, my character set is variable and the length of the initializing combinations is variable from 1-3.
If I round up to the nearest whole number, my offset index, tid = threadIdx.x + .... will be accessing parts of the array that simply do not exist.
How can I handle this problem in such a way that is will still run efficiently and not spawn unnecessary threads that could potentially cause memory problems?
Any constructive input is appreciated.
The code you've posted doesn't seem to do anything significant and includes no cuda code.
Your question appears to be this:
How can I handle this problem in such a way that is will still run efficiently and not spawn unnecessary threads that could potentially cause memory problems?
It's common practice when launching a kernel to "round up" to the nearest increment of threads, perhaps 32, perhaps some multiple of 32, so that an integral number of blocks can be launched. In this case, it's common practice to include a thread check in the kernel code, such as:
__global__ void mykernel(.... int size){
int idx=threadIdx.x + blockDim.x*blockIdx.x;
if (idx < size){
//main body of kernel code here
}
}
In this case, size is your overall problem size (the number of threads that you actually want). The overhead of the additional threads that are doing nothing is normally not a significant performance issue.

set RNG state with openMP and Rcpp

I have a clarification question.
It is my understanding, that sourceCpp automatically passes on the RNG state, so that set.seed(123) gives me reproducible random numbers when calling Rcpp code. When compiling a package, I have to add a set RNG statement.
Now how does this all work with openMP either in sourceCpp or within a package?
Consider the following Rcpp code
#include <Rcpp.h>
#include <omp.h>
// [[Rcpp::depends("RcppArmadillo")]]
// [[Rcpp::export]]
Rcpp::NumericVector rnormrcpp1(int n, double mu, double sigma ){
Rcpp::NumericVector out(n);
for (int i=0; i < n; i++) {
out(i) =R::rnorm(mu,sigma);
}
return(out);
}
// [[Rcpp::export]]
Rcpp::NumericVector rnormrcpp2(int n, double mu, double sigma, int cores=1 ){
omp_set_num_threads(cores);
Rcpp::NumericVector out(n);
#pragma omp parallel for schedule(dynamic)
for (int i=0; i < n; i++) {
out(i) =R::rnorm(mu,sigma);
}
return(out);
}
And then run
set.seed(123)
a1=rnormrcpp1(100,2,3,2)
set.seed(123)
a2=rnormrcpp1(100,2,3,2)
set.seed(123)
a3=rnormrcpp2(100,2,3,2)
set.seed(123)
a4=rnormrcpp2(100,2,3,2)
all.equal(a1,a2)
all.equal(a3,a4)
While a1 and a2 are identical, a3 and a4 are not. How can I adjust the RNG state with the openMP loop? Can I?
To expand on what Dirk Eddelbuettel has already said, it is next to impossible to both generate the same PRN sequence in parallel and have the desired speed-up. The root of this is that generation of PRN sequences is essentially a sequential process where each state depends on the previous one and this creates a backward dependence chain that reaches back as far as the initial seeding state.
There are two basic solutions to this problem. One of them requires a lot of memory and the other one requires a lot of CPU time and both are actually more like workarounds than true solutions:
pregenerated PRN sequence: One thread generates sequentially a huge array of PRNs and then all threads access this array in a manner that would be consistent with the sequential case. This method requires lots of memory in order to store the sequence. Another option would be to have the sequence stored into a disk file that is later memory-mapped. The latter method has the advantage that it saves some compute time, but generally I/O operations are slow, so it only makes sense on machines with limited processing power or with small amounts of RAM.
prewound PRNGs: This one works well in cases when work is being statically distributed among the threads, e.g. with schedule(static). Each thread has its own PRNG and all PRNGs are seeded with the same initial seed. Then each thread draws as many dummy PRNs as its starting iteration, essentially prewinding its PRNG to the correct position. For example:
thread 0: draws 0 dummy PRNs, then draws 100 PRNs and fills out(0:99)
thread 1: draws 100 dummy PRNs, then draws 100 PRNs and fills out(100:199)
thread 2: draws 200 dummy PRNs, then draws 100 PRNs and fills out(200:299)
and so on. This method works well when each thread does a lot of computations besides drawing the PRNs since the time to prewind the PRNG could be substantial in some cases (e.g. with many iterations).
A third option exists for the case when there is a lot of data processing besides drawing a PRN. This one uses OpenMP ordered loops (note that the iteration chunk size is set to 1):
#pragma omp parallel for ordered schedule(static,1)
for (int i=0; i < n; i++) {
#pragma omp ordered
{
rnum = R::rnorm(mu,sigma);
}
out(i) = lots of processing on rnum
}
Although loop ordering essentially serialises the computation, it still allows for lots of processing on rnum to execute in parallel and hence parallel speed-up would be observed. See this answer for a better explanation as to why so.
Yes, sourceCpp() etc and an instantiation of RNGScope so the RNGs are left in a proper state.
And yes one can do OpenMP. But inside of OpenMP segment you cannot control in which order the threads are executed -- so you longer the same sequence. I have the same problem with a package under development where I would like to have reproducible draws yet use OpenMP. But it seems you can't.

Fast way to find the maximum of a float array in OpenCL

I'm having trouble with the simple task of finding the maximum of an array in OpenCL.
__kernel void ndft(/* lots of stuff*/)
{
size_t thread_id = get_global_id(0); // thread_id = [0 .. spectrum_size[
/* MATH MAGIC */
// Now I have float spectrum_abs[spectrum_size] and
// I want the maximum as well as the index holding the maximum
barrier();
// this is the old, sequential code:
if (*current_max_value < spectrum_abs[i])
{
*current_max_value = spectrum_abs[i];
*current_max_freq = i;
}
}
Now I could add if (thread_id == 0) and loop through the entire thing as I would do on a single core system, but since performance is a critical issue (otherwise I wouldn't be doing spectrum calculations on a GPU), is there a faster way to do that?
Returning to the CPU at the end of the kernel above is not an option, because the kernel actually continues after that.
You will need to write a parallel reduction. Split your "large" array into small pieces (a size a single workgroup can effectively process) and compute the min-max in each.
Do this iteratively (involves both host and device code) till you are left with only one set of min/max values.
Note that you might need to write a separate kernel that does this unless the current work-distribution works for this piece of the kernel (see my question to you above).
An alternative if your current work distribution is amenable is to find the min max inside of each workgroup and write it to a buffer in global memory (index = local_id). After a barrier(), simply make the kernel running on thread_id == 0 loop across the reduced results and find the max in it. This will not be the optimal solution, but might be one that fits inside your current kernel.

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