CUDA计算后数组中存在重复值

重复是由操作的实际"输入"值引起的。我不清楚你想要什么数字序列，但这行代码：

g_data[idx] = 3 + idx / 2;

执行整数除法（idx属于int类型，g_data[idx]也是）。

整数除以2的结果意味着"输入"中的每个值都将重复，因此输出中的每个数值也将重复。如果您想查看输入值，请修改上一条cout语句，如下所示：

        std::cout << a[i]<< " : " << i << " " << 3+i/2 << std::endl;

以"模拟"您在内核中进行的输入数据生成。如果你这样做，你会在最后一列数字中看到重复。

编辑：根据下面的评论，idx变量如何生成数字似乎存在一些不确定性。这是一种生成全局唯一线程ID:的规范方法

int idx = blockIdx.x * blockDim.x + threadIdx.x;

在典型的使用中，每个线程都会得到一个唯一的正索引，该索引比"前一个"线程高一个：

0,1,2,3,... 

似乎想要的情况是创建一个看起来像这样的"输入"数据集：

3,5,7,9,...

因此，正确的算法取代了这一点：

g_data[idx] = 3 + idx / 2;

这是吗

g_data[idx] = 3 + idx * 2;

以下是一个完整的例子，其中包含了该更改，以及我建议的先前cout更改：

$ cat t1119.cu
////////////////////////////////////////////////////////////////////////////
//
// Copyright 1993-2015 NVIDIA Corporation.  All rights reserved.
//
// Please refer to the NVIDIA end user license agreement (EULA) associated
// with this source code for terms and conditions that govern your use of
// this software. Any use, reproduction, disclosure, or distribution of
// this software and related documentation outside the terms of the EULA
// is strictly prohibited.
//
////////////////////////////////////////////////////////////////////////////
//
// This sample illustrates the usage of CUDA events for both GPU timing and
// overlapping CPU and GPU execution.  Events are inserted into a stream
// of CUDA calls.  Since CUDA stream calls are asynchronous, the CPU can
// perform computations while GPU is executing (including DMA memcopies
// between the host and device).  CPU can query CUDA events to determine
// whether GPU has completed tasks.
//
// includes, system
#include <stdio.h>
// includes CUDA Runtime
#include <cuda_runtime.h>
// includes, project
#include <helper_cuda.h>
#include <helper_functions.h> // helper utility functions

//set matrix to possible prime values
//evaluate if input is prime, sets variable to 0 if not prime
__global__ void testPrimality(int * g_data) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    g_data[idx] = 3 + idx * 2;
    if (g_data[idx] <= 3) {
        if (g_data[idx] <= 1) {
            g_data[idx] = 0;
        }
    }
    else if (g_data[idx] % 2 == 0 || g_data[idx] % 3 == 0) {
        g_data[idx] = 0;
    }
    else {
        for (unsigned short i = 5; i * i <= g_data[idx]; i += 6) {
            if (g_data[idx] % i == 0 || g_data[idx] % (i + 2) == 0) {
                g_data[idx] = 0;
            }
        }
    }
}
bool correct_output(int *data, const int n, const int x)
{
    for (int i = 0; i < n; i++)
        if (data[i] != x)
        {
            printf("Error! data[%d] = %d, ref = %dn", i, data[i], x);
            return false;
        }
    return true;
}
int main(int argc, char *argv[])
{
    int devID;
    cudaDeviceProp deviceProps;
    printf("[%s] - Starting...n", argv[0]);
    // This will pick the best possible CUDA capable device
    devID = findCudaDevice(argc, (const char **)argv);
    // get device name
    checkCudaErrors(cudaGetDeviceProperties(&deviceProps, devID));
    printf("CUDA device [%s]n", deviceProps.name);
    //const int n = 16 * 1024 * 1024;
    const int n = 1024;
    int nbytes = n * sizeof(int);
    //int value = 1;
    // allocate host memory
    int *a = 0;
    checkCudaErrors(cudaMallocHost((void **)&a, nbytes));
    memset(a, 0, nbytes);

    // allocate device memory
    int *d_a=0;
    checkCudaErrors(cudaMalloc((void **)&d_a, nbytes));
    checkCudaErrors(cudaMemset(d_a, 255, nbytes));
    // set kernel launch configuration
    dim3 threads = dim3(512, 1);
    dim3 blocks  = dim3(n / threads.x, 1);
    // create cuda event handles
    cudaEvent_t start, stop;
    checkCudaErrors(cudaEventCreate(&start));
    checkCudaErrors(cudaEventCreate(&stop));
    StopWatchInterface *timer = NULL;
    sdkCreateTimer(&timer);
    sdkResetTimer(&timer);
    checkCudaErrors(cudaDeviceSynchronize());
    float gpu_time = 0.0f;
    // asynchronously issue work to the GPU (all to stream 0)
    sdkStartTimer(&timer);
    cudaEventRecord(start, 0);
    cudaMemcpyAsync(d_a, a, nbytes, cudaMemcpyHostToDevice, 0);
    //increment_kernel<<<blocks, threads, 0, 0>>>(d_a);
    testPrimality<<<blocks, threads, 0, 0 >>>(d_a);
    cudaMemcpyAsync(a, d_a, nbytes, cudaMemcpyDeviceToHost, 0);
    cudaEventRecord(stop, 0);
    sdkStopTimer(&timer);
    // have CPU do some work while waiting for stage 1 to finish
    unsigned long int counter=0;
    while (cudaEventQuery(stop) == cudaErrorNotReady)
    {
        counter++;
    }
    checkCudaErrors(cudaEventElapsedTime(&gpu_time, start, stop));
    // print the cpu and gpu times
    printf("time spent executing by the GPU: %.2fn", gpu_time);
    printf("time spent by CPU in CUDA calls: %.2fn", sdkGetTimerValue(&timer));
    printf("CPU executed %lu iterations while waiting for GPU to finishn", counter);
    //print values for all allocated memory space
    for (int i = 0; i < n; i++) {
        if (a[i] != 0) {
            std::cout << a[i]<< " : " << i << " " << 3 + i * 2 << std::endl;
        }
    }
    // check the output for correctness
    //bool bFinalResults = correct_output(a, n, value);
    bool bFinalResults = true;

    // release resources
    checkCudaErrors(cudaEventDestroy(start));
    checkCudaErrors(cudaEventDestroy(stop));
    checkCudaErrors(cudaFreeHost(a));
    checkCudaErrors(cudaFree(d_a));
    // cudaDeviceReset causes the driver to clean up all state. While
    // not mandatory in normal operation, it is good practice.  It is also
    // needed to ensure correct operation when the application is being
    // profiled. Calling cudaDeviceReset causes all profile data to be
    // flushed before the application exits
    cudaDeviceReset();
    exit(bFinalResults ? EXIT_SUCCESS : EXIT_FAILURE);
}
$ nvcc -I/usr/local/cuda/samples/common/inc t1119.cu -o t1119
$ cuda-memcheck ./t1119
(excerpted output:)
337 : 167 337
347 : 172 347
349 : 173 349
353 : 175 353
359 : 178 359
367 : 182 367
373 : 185 373
379 : 188 379
383 : 190 383
389 : 193 389
397 : 197 397
401 : 199 401
409 : 203 409
419 : 208 419
421 : 209 421
431 : 214 431
433 : 215 433
439 : 218 439
443 : 220 443
449 : 223 449
457 : 227 457
461 : 229 461
463 : 230 463
467 : 232 467
479 : 238 479
487 : 242 487
491 : 244 491
499 : 248 499
503 : 250 503
509 : 253 509
521 : 259 521
523 : 260 523
541 : 269 541
547 : 272 547
557 : 277 557
563 : 280 563
569 : 283 569
571 : 284 571
577 : 287 577
587 : 292 587
593 : 295 593
599 : 298 599
601 : 299 601
607 : 302 607
613 : 305 613
617 : 307 617
619 : 308 619

如上所述，输出序列中没有重复项。