cudaOccupancyMaxActiveClusters可以根据kernel的cluster大小、block大小和share memory使用量来预测占用率,并以系统中GPU上的最大活动clusters数量来report occupancy。 Occupacy Usage Examples cudaOccupancyMaxActiveBlocksPerMultiprocessor calculates the occupancy of MyKernel. It then reports the occupancy level with the...
当此预留内存用完时,在设备端 Kernel 启动期间分配启动槽(launch slot)时将会失败,并且报cudaErrorLaunchOutOfResources错误,同时分配事件槽(event slot)也会失败并返回cudaErrorMemoryAllocation错误。启动槽的默认数量为2048,应用程序可以通过设置cudaLimitDevRuntimePendingLaunchCount参数来增加启动、事件槽的数量,分配的事...
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===+===+===| | 0 NVIDIA GeForce ... Off | 00000000:02:00.0 Off | N/A | | N/A 47C P0 N/A / N/A | 496Mi...
local memory的上限 可以看到我们测试所得的结果和官方文档上给出的并不一致,通过查阅资料找到了local memory limit的计算公式为: min(amount of loacl memory per thread as documented in seciton G.1 table 12, available GPU memory / number of SMs / maximum resident threads per SM ) 由我在前面给出的...
cudaOccupancyMaxActiveClusters可以根据kernel的cluster大小、block大小和share memory使用量来预测占用率,并以系统中GPU上的最大活动clusters数量来report occupancy。 Occupacy Usage Examples cudaOccupancyMaxActiveBlocksPerMultiprocessor calculates the occupancy of MyKernel. It then reports the occupancy level with the...
memory pitch:2147483647bytes Texture alignment:512bytes Concurrent copy and kernel execution:Yeswith2copyengine(s)Run time limit on kernels:No IntegratedGPUsharing Host Memory:No Support host page-locked memory mapping:Yes Alignment requirementforSurfaces:Yes Device hasECCsupport:Disabled Device supports ...
The initcheck tool can report cases where the GPU performs uninitialized accesses to global memory. The synccheck tool can report cases where the application is attempting invalid usages of synchronization primitives. This document describes the usage of these tools. CUDA-MEMCHECK can be run in ...
6.2.3 Device Memory L2 Access Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3.1 L2 Cache Set-Aside for Persisting Accesses . . . . . . . . . . . . . . . . . . . . . . . 6.2.3.2 L2 Policy for Persisting Accesses . . ....
(local memory) to run your kernel, before you have allocated a single of byte of memory for input and output using pyCUDA. On a GPU with 1Gb of memory, it isn't hard to imagine running out of memory. Your kernel has aenormouslocal memory footprint. Start thinking about way...
This fragmentation will then limit the maximum size of a single allocation that you can request. It's probably not really a question of how you are freeing memory, but more a function of what overhead allocations remain after you free the memory. The fact that you are checking the mem ...