CUDASTDReduce

# Parallel Reduction

Include the Header

Include the Header

CUDASTDReduce_1CUDASTDParallelReductionIncludeTheHeader
Reduce a Range of Items with an Initial Value

Reduce a Range of Items with an Initial Value

CUDASTDReduce_1CUDASTDReduceItemsWithAnInitialValue
Reduce a Range of Items without an Initial Value

Reduce a Range of Items without an Initial Value

CUDASTDReduce_1CUDASTDReduceItemsWithoutAnInitialValue
Reduce a Range of Transformed Items with an Initial Value

Reduce a Range of Transformed Items with an Initial Value

CUDASTDReduce_1CUDASTDReduceTransformedItemsWithAnInitialValue
Reduce a Range of Transformed Items without an Initial Value

Reduce a Range of Transformed Items without an Initial Value

CUDASTDReduce_1CUDASTDReduceTransformedItemsWithoutAnInitialValue

Taskflow provides standard template methods for reducing a range of items on a CUDA GPU.

# Include the Header

You need to include the header file, `taskflow/cuda/algorithm/reduce.hpp`, for using the parallel-reduction algorithm.

    #include<taskflow/cuda/algorithm/reduce.hpp>

# Reduce a Range of Items with an Initial Value

tf::cuda_reduce performs a parallel reduction over a range of elements specified by `[first, last)` using the binary operator `bop` and stores the reduced result in `result`. It represents the parallel execution of the following reduction loop on a GPU:

    while(first!=last){
    *result=bop(*result,*first++);
    }

The variable `result` participates in the reduction loop and must be initialized with an initial value. The following code performs a parallel reduction to sum all the numbers in the given range with an initial value `1000`:

    constsize_tN=1000000;
    int*res=tf::cuda_malloc_shared<int>(1);//result
    int*vec=tf::cuda_malloc_shared<int>(N);//vector

    //initializesthedata
    *res=1000;
    for(size_ti=0;i<N;i++)
    vec[i]=i;
    }

    //createanexecutionpolicy
    tf::cudaStreamstream;
    tf::cudaDefaultExecutionPolicypolicy(stream);

    //queriestherequiredbuffersizetoreduceNelementsusingthegivenpolicy
    autobytes=policy.reduce_bufsz<int>(N);
    autobuffer=tf::cuda_malloc_device<std::byte>(bytes);

    //*res=1000+(0+1+2+3+4+...+N-1)
    tf::cuda_reduce(policy,
    vec,vec+N,res,[]__device__(inta,intb){returna+b;},buffer
    );

    //synchronizetheexecution
    stream.synchronize();

    //deletethememory
    cudaFree(buffer);
    cudaFree(res);
    cudaFree(vec);

The reduce algorithm runs asynchronously through the stream specified in the execution policy. You need to synchronize the stream to obtain correct results. Since the GPU reduction algorithm may require extra buffer to store the temporary results, you need to provide a buffer of size at least larger or equal to the value returned from `tf::cudaDefaultExecutionPolicy::reduce_bufsz`.

You must keep the buffer alive before the reduction completes.

# Reduce a Range of Items without an Initial Value

tf::cuda_uninitialized_reduce performs a parallel reduction over a range of items without an initial value. This method represents a parallel execution of the following reduction loop on a GPU:

    *result=*first++;//noinitialvaluestoparticipateinthereductionloop
    while(first!=last){
    *result=bop(*result,*first++);
    }

The variable `result` is directly assigned the reduced value without any initial value participating in the reduction loop. The following code performs a parallel reduction to sum all the numbers in the given range without any initial value:

    constsize_tN=1000000;
    int*res=tf::cuda_malloc_shared<int>(1);//result
    int*vec=tf::cuda_malloc_shared<int>(N);//vector

    //initializesthedata
    for(size_ti=0;i<N;i++)
    vec[i]=i;
    }

    //createanexecutionpolicy
    tf::cudaStreamstream;
    tf::cudaDefaultExecutionPolicypolicy(stream);

    //queriestherequiredbuffersizetoreduceNelementsusingthegivenpolicy
    autobytes=policy.reduce_bufsz<int>(N);
    autobuffer=tf::cuda_malloc_device<std::byte>(bytes);

    //*res=0+1+2+3+4+...+N-1
    tf::cuda_uninitialized_reduce(policy,
    vec,vec+N,res,[]__device__(inta,intb){returna+b;},buffer
    );

    //synchronizetheexecution
    stream.synchronize();

    //deletethebuffer
    cudaFree(res);
    cudaFree(vec);
    cudaFree(buffer);

# Reduce a Range of Transformed Items with an Initial Value

tf::cuda_transform_reduce performs a parallel reduction over a range of transformed elements specified by `[first, last)` using a binary reduce operator `bop` and a unary transform operator `uop`. It represents the parallel execution of the following reduction loop on a GPU:

    while(first!=last){
    *result=bop(*result,uop(*first++));
    }

The variable `result` participates in the reduction loop and must be initialized with an initial value. The following code performs a parallel reduction to sum all the transformed numbers multiplied by `10` in the given range with an initial value `1000`:

    constsize_tN=1000000;
    int*res=tf::cuda_malloc_shared<int>(1);//result
    int*vec=tf::cuda_malloc_shared<int>(N);//vector

    //initializesthedata
    *res=1000;
    for(size_ti=0;i<N;i++){
    vec[i]=i;
    }

    //createanexecutionpolicy
    tf::cudaStreamstream;
    tf::cudaDefaultExecutionPolicypolicy(stream);

    //queriestherequiredbuffersizetoreduceNelementsusingthegivenpolicy
    autobytes=policy.reduce_bufsz<int>(N);
    autobuffer=tf::cuda_malloc_device<std::byte>(bytes);

    //*res=1000+(0*10+1*10+2*10+3*10+4*10+...+(N-1)*10)
    tf::cuda_transform_reduce(policy,
    vec,vec+N,res,
    []__device__(inta,intb){returna+b;},
    []__device__(inta){returna*10;},
    buffer
    );

    //synchronizetheexecution
    stream.synchronize();

    //deletethebuffer
    cudaFree(res);
    cudaFree(vec);
    cudaFree(buffer);

# Reduce a Range of Transformed Items without an Initial Value

tf::cuda_transform_uninitialized_reduce performs a parallel reduction over a range of transformed items without an initial value. This method represents a parallel execution of the following reduction loop on a GPU:

    *result=*first++;//noinitialvaluestoparticipateinthereductionloop
    while(first!=last){
    *result=bop(*result,uop(*first++));
    }

The variable `result` is directly assigned the reduced value without any initial value participating in the reduction loop. The following code performs a parallel reduction to sum all the transformed numbers multiplied by `10` in the given range without any initial value:

    constsize_tN=1000000;
    int*res=tf::cuda_malloc_shared<int>(1);//result
    int*vec=tf::cuda_malloc_shared<int>(N);//vector

    //initializesthedata
    for(size_ti=0;i<N;i++){
    vec[i]=i;
    }

    //createanexecutionpolicy
    tf::cudaStreamstream;
    tf::cudaDefaultExecutionPolicypolicy(stream);

    //queriestherequiredbuffersizetoreduceNelementsusingthegivenpolicy
    autobytes=policy.reduce_bufsz<int>(N);
    autobuffer=tf::cuda_malloc_device<std::byte>(bytes);

    //*res=0*10+1*10+2*10+3*10+4*10+...+(N-1)*10
    tf::cuda_uninitialized_reduce(policy,
    vec,vec+N,res,
    []__device__(inta,intb){returna+b;},
    []__device__(inta){returna*10;},
    buffer
    );

    //synchronizetheexecution
    stream.synchronize();

    //deletethedata
    cudaFree(res);
    cudaFree(vec);
    cudaFree(buffer);