This snippet execute the printf statement, it seems to be related to prime number sizes (86413 is a prime). This can be worked around by switching to a nd_range ...Compiler flags : -fsycl -fsycl-targets=nvptx64-nvidia-cudaAny idea why this is the case ? Thank...
x;//获取 线程在 2-d块儿中的y索引 ,to for (int i = 0; i < A->width; ++i) { Cvalue += getElement(A, row, i) * getElement(B, i, col); } if(row>1000){ printf("%lf\n",Cvalue); } setElement(C, row, col, Cvalue); } 核函数调用: 通过手动设置需要的block size和...
GPU printf FIFO size cudaLimitMallocHeapSize = 0x02 GPU malloc heap size cudaLimitDevRuntimeSyncDepth = 0x03 GPU device runtime synchronize depth cudaLimitDevRuntimePendingLaunchCount = 0x04 GPU device runtime pending launch count cudaLimitMaxL2FetchGranularity = 0x05 A value between 0 an...
N);break;default:printf("Error: wrong task\n");exit(1);break;}CHECK(cudaEventRecord(stop));...
If the register that was used to store the value of a variable has been reused since the last time the variable was seen as live, then the reported value will be wrong. Therefore, any value printed using the option will be marked as "(possibly)". ‣ (cuda-gdb) set cuda value_...
(93): error: linkage specification is incompatible with previous "vsnprintf" (declared at line 389 of /usr/include/stdio.h) vsnprintf (char * __restrict const __attribute__ ((__pass_object_size__ (1 > 1))) __s, size_t __n, const char *__restrict __fmt, __gnuc_va_list _...
graphMemoryFootprint.cu(274): error: variable "printf" has already been defined graphMemoryFootprint.cu(275): error: a value of type "const char *" cannot be used to initialize an entity of type "int" Error limit reached. 100 errors detected in the compilati...
NGC Containers:Validator for NVIDIA GPU Operator SDK:Nsight Compute SDK:Nsight Visual Studio Code Edition SDK:CUPTI Discuss (7) +18 Like Tags Data Center / Cloud|Simulation / Modeling / Design|HPC / Scientific Computing|CUDA|HPC SDK|Intermediate Technical|Tutorial|Accelerated Computing Libraries|featu...
printf("dx value : %f\n",dx); That is host code. Understanding what is wrong there is simply a matter of C/C++ coding, not anything to do with CUDA. If you like, compare it with the way you wrote the printf statement in your kernel code, to see if any of the differences seem ...
printf("After : Vector 0, 1 .. N-1: %f %f .. %f\n", h_vec[0], h_vec[1], h_vec[BLOCKS*THREADS-1]); cudaFree(d_vec); free(h_vec); exit(0); } This code contains a CUDA kernel calledaddToVectorthat performs a simple add of a value to each elem...