当我们想要使用cupy对代码进行加速时,一种可行的做法是,首先定义函数名XXX 然后利用cuda.compile_with_cache将strKernel中的代码进行编译,得到调用接口: 1.cupy_krl = cupy.cuda.compile_with_cache(strKernel) 2.cupy_launchr = cupy_krl.get_function(strFunct
如果你的代码中使用了 cupy.cuda.compile_with_cache,你需要更新你的代码以适应新的API。具体来说,你可以使用 cupy.RawKernel 来替代。 使用cupy.RawKernel 替代: 从CuPy v10开始,你可以使用 cupy.RawKernel 来编译和缓存CUDA内核。cupy.RawKernel 接受源代码字符串和函数名称作为参数,并返回一个可以直接调用的...
Size of L2 cache in bytes cudaDevAttrMaxThreadsPerMultiProcessor = 39 Maximum resident threads per multiprocessor cudaDevAttrAsyncEngineCount = 40 Number of asynchronous engines cudaDevAttrUnifiedAddressing = 41 Device shares a unified address space with the host cudaDevAttrMaxTexture1DLayeredWidth...
CU_DEVICE_ATTRIBUTE_MAX_PERSISTING_L2_CACHE_SIZE = 108 Maximum L2 persisting lines capacity setting in bytes. CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE = 109 Maximum value of CUaccessPolicyWindow::num_bytes. CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WITH_CUDA_VMM_SUPPORTED = 110 Device su...
优化代码：__device__voidwarpReduce(volatilefloat*cache,unsignedinttid){cache[tid]+=cache[tid+32]...
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. (.venv) reply@reply-GP66-Leopard-11UH:~/dev/chatbot-rag$ nvidia-smi Tue Nov 7 02:17:55 2023 It does not appear that the GPU is being over-utilized. +---+ |...
(self.llvmir,opt=3,arch=arch,-->378**self._extra_options)379self.cache[cc]=ptx380ifconfig.DUMP_ASSEMBLY:~.conda\envs\tensorflow\lib\site-packages\numba\cuda\cudadrv\nvvm.pyinllvm_to_ptx(llvmir,**opts)498cu.add_module(libdevice.get())499-->500ptx=cu.compile(**opts)501#XXXremove ...
When the device driver just-in-time compiles some PTX code for some application, it automatically caches a copy of the generated binary code in order to avoid repeating the compilation in subsequent invocations of the application. The cache - referred to as compute cache - is automatically inval...
in cupy.core.core.compile_with_cache File "/home/tamouze/anaconda2/envs/testing-env/lib/python2.7/site-packages/cupy/cuda/compiler.py", line 164, in compile_with_cache ptx = compile_using_nvrtc(source, options, arch) File "/home/tamouze/anaconda2/envs/testing-env/lib/python2.7/site-pa...
(此外,还有home目录下~/.nv/ComputeCache的一些文件被使用,这个目录是用来缓存PTX伪汇编JIT编译后的二进制文件fat binaries,与我们当前的问题无关,感兴趣的朋友可参考Mark Harris的《CUDA Pro Tip: Understand Fat Binaries and JIT Caching》。)要使CUDA runtime API能被正常执行,需要完成上述动态库的加载、内核...