CUDASTDMerge

# Parallel Merge

Include the Header

Include the Header

CUDASTDMerge_1CUDASTDMergeIncludeTheHeader
Merge Two Sorted Ranges of Items

Merge Two Sorted Ranges of Items

CUDASTDMerge_1CUDASTDMergeItems
Merge Two Sorted Ranges of Key-Value Items

Merge Two Sorted Ranges of Key-Value Items

CUDASTDMerge_1CUDASTDMergeKeyValueItems

Taskflow provides standalone template methods for merging two sorted ranges of items into a sorted range of items.

# Include the Header

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

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

# Merge Two Sorted Ranges of Items

tf::cuda_merge merges two sorted ranges of items into a sorted range. The following code merges two sorted arrays `input_1` and `input_2`, each of 1000 items, into a sorted array `output` of 2000 items.

    constsize_tN=1000;
    int*input_1=tf::cuda_malloc_shared<int>(N);//inputvector1
    int*input_2=tf::cuda_malloc_shared<int>(N);//inputvector2
    int*output=tf::cuda_malloc_shared<int>(2*N);//outputvector

    //initializesthedata
    for(size_ti=0;i<N;i++){
    input_1[i]=rand()%100;
    input_2[i]=rand()%100;
    }
    std::sort(input_1,input1+N);
    std::sort(input_2,input2+N);

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

    //queriestherequiredbuffersizetomergetwoN-elementsortedvectors
    autobytes=policy.merge_bufsz(N,N);
    autobuffer=tf::cuda_malloc_device<std::byte>(bytes);

    //mergeinput_1andinput_2tooutput
    tf::cuda_merge(policy,
    input_1,input_1+N,input_2,input_2+N,output,
    []__device__(inta,intb){returna<b;},//comparator
    buffer
    );

    //synchronizestheexecutionandverifiestheresult
    stream.synchronize();

    //verifytheresult
    assert(std::is_sorted(output,output+2*N));

    //deletethebuffer
    cudaFree(input1);
    cudaFree(input2);
    cudaFree(output);
    cudaFree(buffer);

The merge algorithm runs asynchronously through the stream specified in the execution policy. You need to synchronize the stream to obtain correct results. Since the GPU merge 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::merge_bufsz`. The buffer size depends only on the two input vector sizes.

You must keep the buffer alive before the merge call completes.

# Merge Two Sorted Ranges of Key-Value Items

tf::cuda_merge_by_key performs key-value merge over two sorted ranges in a similar way to tf::cuda_merge; additionally, it copies elements from the two ranges of values associated with the two input keys, respectively. The following code performs key-value merge over `a` and `b:`

    constsize_tN=2;
    int*a_keys=tf::cuda_malloc_shared<int>(N);
    int*a_vals=tf::cuda_malloc_shared<int>(N);
    int*b_keys=tf::cuda_malloc_shared<int>(N);
    int*b_vals=tf::cuda_malloc_shared<int>(N);
    int*c_keys=tf::cuda_malloc_shared<int>(2*N);
    int*c_vals=tf::cuda_malloc_shared<int>(2*N);

    //initializesthedata
    a_keys[0]=8,a_keys[1]=1;
    a_vals[0]=1,a_vals[1]=2;
    b_keys[0]=3,b_keys[1]=7;
    b_vals[0]=3,b_vals[1]=4;

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

    //queriestherequiredbuffersizetomergetwoN-elementsortedvectorsbykeys
    autobytes=policy.merge_bufsz(N,N);
    autobuffer=tf::cuda_malloc_device<std::byte>(bytes);

    //mergekeysandvaluesofaandbtoc
    tf::cuda_merge_by_key(
    policy,
    a_keys,a_keys+N,a_vals,
    b_keys,b_keys+N,b_vals,
    c_keys,c_vals,
    []__device__(inta,intb){returna<b;},//comparator
    buffer
    );

    //waitforthemergetocomplete
    stream.synchronize();

    //now,c_keys={1,3,7,8}
    //now,c_vals={2,3,4,1}

    //deletethedevicememory
    cudaFree(buffer);
    cudaFree(a_keys);
    cudaFree(b_keys);
    cudaFree(c_keys);
    cudaFree(a_vals);
    cudaFree(b_vals);
    cudaFree(c_vals);

Since the GPU merge 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::merge_bufsz`. The buffer size depends only on the two input vector sizes.