我正在学习JCuda并使用JCuda样本进行学习。
当我使用JCuda研究KMeans算法代码时，我在执行行cuCtxSynchronize（）时得到了“CUDA_ERROR_ILLEGAL_ADDRESS”;
让我很困惑。我该如何解决？
这是KMeansKernel.cu
extern "C"
__global__ void add(int n, float *a, float *b, float *sum)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i<n)
{
sum[i] = a[i] + b[i];
}
}
主要方法（我的班级名为“CUDA”）：
public static void main(String[] args){
// omit some code which input kinds of parameters
try {
// Open image file
BufferedImage bi = ImageIO.read(picFiles);
if (bi == null) {
System.out.println("ERROR: File input error.");
return;
}
// Read image data
int length = bi.getWidth() * bi.getHeight();
int[] imageProperty = new int[length*5];
int[] pixel;
int count = 0;
for (int y = 0; y < bi.getHeight(); y++) {
for (int x = 0; x < bi.getWidth(); x++) {
pixel = bi.getRaster().getPixel(x, y, new int[4]);
imageProperty[count*5 ] = pixel[0];
imageProperty[count*5+1] = pixel[1];
imageProperty[count*5+2] = pixel[2];
imageProperty[count*5+3] = x;
imageProperty[count*5+4] = y;
count++;
}
}
//setup
JCudaDriver.setExceptionsEnabled(true);
// Create the PTX file
String ptxFileName;
try
{
ptxFileName = preparePtxFile("KmeansKernel.cu");
}
catch (IOException e)
{
System.out.println("Warning...");
System.out.println(e.getMessage());
System.out.println("Exiting...");
return;
}
cuInit(0);
CUdevice device = new CUdevice();
cuDeviceGet(device, 0);
CUcontext context = new CUcontext();
cuCtxCreate(context, 0, device);
CUmodule module = new CUmodule();
cuModuleLoad(module, ptxFileName);
CUfunction kmeansFunction = new CUfunction();
System.out.println("x");
cuModuleGetFunction(kmeansFunction, module, "add");
//copy host input to device
CUdeviceptr imageDevice = new CUdeviceptr();
cuMemAlloc(imageDevice, imageProperty.length * Sizeof.INT);
cuMemcpyHtoD(imageDevice, Pointer.to(imageProperty), imageProperty.length * Sizeof.INT);
int blockSizeX = 256;
int gridSizeX = (int) Math.ceil((double)(imageProperty.length / 5) / blockSizeX);
long et = System.currentTimeMillis();
System.out.println(((double)(et-st)/1000.0) + "s");
for (int k = startClusters; k <= endClusters; k++) {
long startTime = System.currentTimeMillis();
int[] clusters = new int[length];
int[] c = new int[k*5];
int h = 0;
for(int i = 0; i < k; i++) {
c[i*5] = imageProperty[h*5];
c[i*5+1] = imageProperty[h*5+1];
c[i*5+2] = imageProperty[h*5+2];
c[i*5+3] = imageProperty[h*5+3];
c[i*5+4] = imageProperty[h*5+4];
h += length / k;
}
double tolerance = 1e-4;
**//got warning in following line
CUDA.KmeansKernel(kmeansFunction, imageDevice, imageProperty, clusters, c, k, tolerance, distanceWeight, colorWeight, blockSizeX, gridSizeX);**
int[] output = calculateAveragePixels(imageProperty, clusters);
BufferedImage outputImage = new BufferedImage(bi.getWidth(), bi.getHeight(), BufferedImage.TYPE_INT_RGB);
for (int i = 0; i < length; i++) {
int rgb = output[i*5];
rgb = (rgb * 256) + output[i*5+1];
rgb = (rgb * 256) + output[i*5+2];
outputImage.setRGB(i%bi.getWidth(), i/bi.getWidth(), rgb);
}
String fileName = (picFiles.getName()) + ".bmp";
File outputFile = new File("output/" + fileName);
ImageIO.write(outputImage, "BMP", outputFile);
long runTime = System.currentTimeMillis() - startTime;
System.out.println("Completed iteration k=" + k + " in " + ((double)runTime/1000.0) + "s");
}
System.out.println("Files saved to " + outputDirectory.getAbsolutePath() + "\\");
cuMemFree(imageDevice);
} catch (IOException e) {
e.printStackTrace();
}
}
方法KmeansKernel：
private static void KmeansKernel(CUfunction kmeansFunction, CUdeviceptr imageDevice, int[] imageProperty, int[] clusters, int[] c,
int k, double tolerance, double distanceWeight, double colorWeight,
int blockSizeX, int gridSizeX) {
CUdeviceptr clustersDevice = new CUdeviceptr();
cuMemAlloc(clustersDevice, clusters.length * Sizeof.INT);
// Alloc device output
CUdeviceptr centroidPixels = new CUdeviceptr();
cuMemAlloc(centroidPixels, k * 5 * Sizeof.INT);
CUdeviceptr errorDevice = new CUdeviceptr();
cuMemAlloc(errorDevice, Sizeof.DOUBLE * clusters.length);
int[] c1 = new int[k*5];
cuMemcpyHtoD(centroidPixels, Pointer.to(c), Sizeof.INT * 5 * k);
// begin algorithm
int[] counts = new int[k];
double old_error, error = Double.MAX_VALUE;
int l = 0;
do {
l++;
old_error = error;
error = 0;
Arrays.fill(counts, 0);
Arrays.fill(c1, 0);
cuMemcpyHtoD(centroidPixels, Pointer.to(c), k * 5 * Sizeof.INT);
Pointer kernelParameters = Pointer.to(
Pointer.to(new int[] {clusters.length}),
Pointer.to(new int[] {k}),
Pointer.to(new double[] {colorWeight}),
Pointer.to(new double[] {distanceWeight}),
Pointer.to(errorDevice),
Pointer.to(imageDevice),
Pointer.to(centroidPixels),
Pointer.to(clustersDevice)
);
cuLaunchKernel(kmeansFunction,
gridSizeX, 1, 1,
blockSizeX, 1, 1,
0, null,
kernelParameters, null
);
**cuCtxSynchronize(); //got warning here.why?**
cuMemcpyDtoH(Pointer.to(clusters), clustersDevice, Sizeof.INT*clusters.length);
for (int i = 0; i < clusters.length; i++) {
int cluster = clusters[i];
counts[cluster]++;
c1[cluster*5] += imageProperty[i*5];
c1[cluster*5+1] += imageProperty[i*5+1];
c1[cluster*5+2] += imageProperty[i*5+2];
c1[cluster*5+3] += imageProperty[i*5+3];
c1[cluster*5+4] += imageProperty[i*5+4];
}
for (int i = 0; i < k; i++) {
if (counts[i] > 0) {
c[i*5] = c1[i*5] / counts[i];
c[i*5+1] = c1[i*5+1] / counts[i];
c[i*5+2] = c1[i*5+2] / counts[i];
c[i*5+3] = c1[i*5+3] / counts[i];
c[i*5+4] = c1[i*5+4] / counts[i];
} else {
c[i*5] = c1[i*5];
c[i*5+1] = c1[i*5+1];
c[i*5+2] = c1[i*5+2];
c[i*5+3] = c1[i*5+3];
c[i*5+4] = c1[i*5+4];
}
}
double[] errors = new double[clusters.length];
cuMemcpyDtoH(Pointer.to(errors), errorDevice, Sizeof.DOUBLE*clusters.length);
error = sumArray(errors);
System.out.println("" + l + " iterations");
} while (Math.abs(old_error - error) > tolerance);
cuMemcpyDtoH(Pointer.to(clusters), clustersDevice, clusters.length * Sizeof.INT);
cuMemFree(errorDevice);
cuMemFree(centroidPixels);
cuMemFree(clustersDevice);
}
堆栈追踪：
Exception in thread "main" jcuda.CudaException: CUDA_ERROR_ILLEGAL_ADDRESS
at jcuda.driver.JCudaDriver.checkResult(JCudaDriver.java:330)
at jcuda.driver.JCudaDriver.cuCtxSynchronize(JCudaDriver.java:1938)
at com.test.CUDA.KmeansKernel(CUDA.java:269)
at com.test.CUDA.main(CUDA.java:184)
答案 0 :(得分：1)
正如@talonmies所提到的，传递给kernelParameters方法的cuLaunchKernel与add内核函数签名不一致。
您在cuCtxSynchronize收到错误，因为CUDA执行模型是异步的：cuLaunchKernel立即返回，并且设备上内核的实际执行是异步的。 cuCtxSynchronize文档内容为：
请注意，此函数还可能返回先前异步启动的错误代码。
第二个kernelParameters条目是int k，其中add方法的第二个参数是pointer to float，因此很可能是非法访问错误。