Parallelization of a for loop consisting of Thrust Transforms - c++

Question

I've implemented a for loop consisting of several Thrust transformations. My aim is to calculate r[i] for each value of i from 0 to N. To put simply, r is a column vector and each of its elements can be calculated independently.
Therefore, I'm looking a way of parallelizing the for loop given below:
for(int i=0; i < N; i++) {
thrust::device_vector<float> P(N, 0.0);
thrust::device_vector<int> corr_col_indices_d(col_indices.begin() + row_begin[i], col_indices.begin() + row_begin[i+1]); // indices of the columns
thrust::device_vector<float> corr_values_d(values_d.begin() + row_begin[i], values_d.begin() + row_begin[i+1]); // values of the columns
// P[j] = corr_values_d[k] if j is in corr_col_indices_d, else 0 (increment k if j is in corr_col_indices_d)
thrust::scatter(corr_values_d.begin(), corr_values_d.end(), corr_col_indices_d.begin(), P.begin());
r2[i] = thrust::inner_product(P.begin(), P.end(), r1.begin(), 0.0f);
}
1) After lots of googling, roaming around Stackoverflow and NVIDIA, I attempted to put all successive transformations into a bigger "transform" with a loop variable i.
auto counting_iter = thrust::make_counting_iterator(0);
thrust::transform(counting_iter, counting_iter + N, r2.begin(), [&](int i) {
thrust::device_vector<float> P(N, 0.0);
thrust::device_vector<int> corr_col_indices_d(col_indices.begin() + row_begin[i], col_indices.begin() + row_begin[i+1]); /
thrust::device_vector<float> corr_values_d(values_d.begin() + row_begin[i], values_d.begin() + row_begin[i+1]);
thrust::scatter(corr_values_d.begin(), corr_values_d.end(), corr_col_indices_d.begin(), P.begin());
thrust::transform(P.begin(), P.end(), r1.begin(), P.begin(), thrust::multiplies<float>());
return thrust::reduce(P.begin(), P.end());
});
Unfortunately it doesn't work. Either the there is no such a thing as giving transformations like this, or my syntax is wrong.
2) Then I tried to create a functor that takes all these device_vectors as input and operates on them. As stated here, it's not possible to pass device_vectors to a functor from outside - therefore I attempted to give them as raw pointers.
struct loop {
// constructor that takes a vector as a parameter
__host__ __device__
loop(int *t_row_begin, int *t_col_indices, float*t_values, float *r1):
t_row_begin_(t_row_begin), t_col_indices_(t_col_indices), t_values_(t_values), r1_(r1) {}
// member variable to store the vector
int *t_row_begin_;
int *t_col_indices_;
float *t_values_;
float *r1_;
__host__ __device__
float operator()(int i) const {
thrust::device_vector<float> P(N, 0.0);
thrust::device_vector<int> corr_col_indices_d(t_col_indices_ + t_row_begin_[i], t_col_indices_ + t_row_begin_[i + 1]); // indices of the columns
thrust::device_vector<float> corr_values_d(t_values_ + t_row_begin_[i], t_values_ + t_row_begin_[i+1]); // values of the columns
thrust::scatter(corr_values_d.begin(), corr_values_d.end(), corr_col_indices_d.begin(), P.begin());
return thrust::inner_product(P.begin(), P.end(), r1.begin(), 0.0f);
}
};
and the loop itself:
loop lp(thrust::raw_pointer_cast(row_begin_d.data()),
thrust::raw_pointer_cast(col_indices_d.data()),
thrust::raw_pointer_cast(values_d.data()),
thrust::raw_pointer_cast(r1.data()));
auto iter = thrust::make_counting_iterator(0);
// perform the operations for each iteration of the loop using transform
thrust::transform(iter, iter + N, r2.begin(), lp);
3) I even tried passing arguments to operator rather than the constructor of the functor:
struct loop {
__host__ __device__
float operator()(int i, thrust::device_vector<int>& col_indices, thrust::device_vector<float>& values_d, thrust::device_vector<int>& row_begin, thrust::device_vector<float>& r1) const {
thrust::device_vector<float> P(N, 0.0);
thrust::device_vector<int> corr_col_indices_d(col_indices.begin() + row_begin[i], col_indices.begin() + row_begin[i+1]); // indices of the columns
thrust::device_vector<float> corr_values_d(values_d.begin() + row_begin[i], values_d.begin() + row_begin[i+1]); // values of the columns
thrust::scatter(corr_values_d.begin(), corr_values_d.end(), corr_col_indices_d.begin(), P.begin());
return thrust::inner_product(P.begin(), P.end(), r1.begin(), 0.0f);
}
};
auto iter = thrust::make_counting_iterator(0);
thrust::transform(iter, iter + N, r2.begin(),
thrust::make_transform_iterator(iter, loop()),
thrust::make_zip_iterator(thrust::make_tuple(col_indices, values_d, row_begin, r1)));
None of them compiles and all those complicated error messages don't really help. So, I'm looking for some assistance at this point.
CUDA version: 11.2Thrust version: 1.10.0
Edit: In case you wonder, those vectors correspond to components of CSR matrix representation:
vector<int> row_begin;
vector<float> values;
vector<int> col_indices;
Updates
Fused transform and reduce to inner_product. as suggested by #paleonix.

Answer 1

To show my thought process when refactoring your code, I provide you with multiple steps/intermediate solutions:
Get rid of allocations inside the loop, they are expensive while you do not need copies of the rows and P can be reused:
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/scatter.h>
#include <thrust/inner_product.h>
void foo(int N,
thrust::host_vector<int> const &row_begin,
thrust::device_vector<int> const &col_indices,
thrust::device_vector<float> const &values_d,
thrust::device_vector<float> const &r1,
thrust::host_vector<float> &r2) {
thrust::device_vector<float> P(N);
for(int i = 0; i < N; ++i) {
thrust::fill(P.begin(), P.end(), 0.0f);
// P[j] = corr_values_d[k] if j is in corr_col_indices_d, else 0 (increment k if j is in corr_col_indices_d)
thrust::scatter(values_d.cbegin() + row_begin[i],
values_d.cbegin() + row_begin[i+1],
col_indices.cbegin() + row_begin[i],
P.begin());
r2[i] = thrust::inner_product(P.cbegin(), P.cend(),
r1.cbegin(),
0.0f);
}
}
Use a permutation iterator on r1 instead of scattering the values into P. This is much more efficient as it avoids unnecessary memory accesses. The scattered and therefore non-coalesced access is expensive, so if you would access the data more than once in this fashion, using thrust::scatter could be better again.
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/inner_product.h>
#include <thrust/iterator/permutation_iterator.h>
void foo(int N,
thrust::host_vector<int> const &row_begin,
thrust::device_vector<int> const &col_indices,
thrust::device_vector<float> const &values_d,
thrust::device_vector<float> const &r1,
thrust::host_vector<float> &r2) {
auto const r1_iter =
thrust::make_permutation_iterator(
r1.cbegin(),
col_indices.cbegin());
for(int i = 0; i < N; ++i) {
r2[i] =
thrust::inner_product(
values_d.cbegin() + row_begin[i],
values_d.cbegin() + row_begin[i+1],
r1_iter + row_begin[i],
0.0f);
}
}
There is not much parallelism left in the inner_product. So do it sequentially and parallelize the outer loop:
#include <thrust/device_vector.h>
#include <thrust/inner_product.h>
#include <thrust/transform.h>
#include <thrust/iterator/permutation_iterator.h>
#include <thrust/iterator/counting_iterator.h>
void foo(int N,
thrust::device_vector<int> const &row_begin,
thrust::device_vector<int> const &col_indices,
thrust::device_vector<float> const &values_d,
thrust::device_vector<float> const &r1,
thrust::device_vector<float> &r2) {
auto const row_begin_ptr = row_begin.data();
auto const col_indices_ptr = col_indices.data();
auto const values_d_ptr = values_d.data();
auto const r1_iter =
thrust::make_permutation_iterator(
r1.cbegin(),
col_indices.cbegin());
thrust::transform(
thrust::make_counting_iterator(0),
thrust::make_counting_iterator(0) + N,
r2.begin(),
[=] __host__ __device__ (int i){
return thrust::inner_product(thrust::seq,
values_d_ptr + row_begin_ptr[i],
values_d_ptr + row_begin_ptr[i+1],
r1_iter + row_begin_ptr[i],
0.0f);
});
}
While above solution should be sufficient for e.g. banded matrices where the rows are each very small and regular, irregularities like single long rows will make this solution quite inefficient again due to work-imbalance and warp divergence. The alternative is to use a segmented/batched reduction as implemented by thrust::reduce_by_key. To use reduce_by_key here, one would need to "decompress" the CSR matrix (transforming row offsets into keys), even though Thrust might go back to row offsets under the hood either way to use CUB in the back-end.
To avoid this inefficiency, I used CUB directly via cub::DeviceSegmentedReduce::Sum. To still fuse the transform/multiplication into the reduction, one can use a transform iterator. I also ditched the permutation iterator and implemented the gather directly in the transform iterator:
#include <cub/cub.cuh>
#include <thrust/device_vector.h>
#include <thrust/iterator/counting_iterator.h>
#include <thrust/iterator/transform_iterator.h>
void foo(int N,
thrust::device_vector<int> const &row_begin,
thrust::device_vector<int> const &col_indices,
thrust::device_vector<float> const &values_d,
thrust::device_vector<float> const &r1,
thrust::device_vector<float> &r2) {
auto const col_indices_ptr = col_indices.data();
auto const values_d_ptr = values_d.data();
auto const r1_ptr = r1.data();
auto const corr_iter =
thrust::make_transform_iterator(
thrust::make_counting_iterator(0),
[=] __host__ __device__ (int j){
return values_d_ptr[j] * r1_ptr[col_indices_ptr[j]];
});
// Determine temporary storage
size_t temp_storage_bytes = 0;
cub::DeviceSegmentedReduce::Sum(nullptr, temp_storage_bytes,
corr_iter,
r2.begin(),
N,
row_begin.cbegin(), row_begin.cbegin() + 1);
// Allocate temporary storage
thrust::device_vector<char> d_temp_storage(temp_storage_bytes);
// Run sum-reduction
cub::DeviceSegmentedReduce::Sum(thrust::raw_pointer_cast(d_temp_storage.data()),
temp_storage_bytes,
corr_iter,
r2.begin(),
N,
row_begin.cbegin(), row_begin.cbegin() + 1);
}
Avoiding Temporary Buffer Initialization (and Allocation)
The only thing missing in this last solution for "ideal" performance is that the temporary storage is unnecessarily initialized. This can be avoided by using a custom allocator as shown in the Thrust example uninitialized_vector.cu. I did not include it in above code to avoid the bloat.
An even nicer solution is the rmm::device_buffer from the RAPIDS Memory Manager, but this one is not included in the CUDA Toolkit.
In the future libcudac++ will hopefully give us a similarly nice C++ option, as they are working on memory resources at the moment.
If this operation is done repeatedly, one can also just reuse the temporary memory. Even when not using CUB directly, one can achieve this using a pool memory resource. See cuda/custom_temporary_allocation.cu and mr_basic.cu