What it most likely means is that the driver is working fine, but the toolkit libraries aren’t. There are two APIs in CUDA, the direct driver API and the runtime API. The deviceQuery type stuff uses the driver API which talks straight to the driver (libcuda) and looks like ...
According to theRuntime API documentation, I know I can’t directly call the CUDA function in the callback of the HOST node. So that I can’t directly callcudaLaunch()to launch an executable graph in the HOST node either. After consideration, I think I can create a child thread in the...
My GPU does not support CUDA. Can I still use FluidX3D?Yes. FluidX3D uses OpenCL 1.2 and not CUDA, so it runs on any GPU from any vendor since around 2012. I don't have a dedicated graphics card at all. Can I still run FluidX3D on my PC/laptop?Yes. FluidX3D also runs on all...
to(dtype) mat1_cuda = mat1.to('cuda') tasks = [("torch.all(mat1)", "torch.all CPU"), # In CUDA, this path is taken only when training is False. ("torch.all(mat1_cuda)", "torch.all CUDA")] timers = [Timer(stmt=stmt, num_threads=num_threads, label=f"All {dtype}", ...
I completely missed that the cl_intel_subgroups extension is also supported on OpenCL 1.2 Haswell IGPs -- at least it is on the HD4600. I just ported a bunch of kernels from CUDA to the Haswell/Broadwell and having shuffle operations made the job pretty ...
Well I can't see any reason for a gap in performance between CUDA and OpenCL (except bad implementation). All the wrapping from C++ to OpenCL kernels and call will have to do exactly the same work as the CUDA compiler is currently doing so I would not see it as a penalty since it...
Ascend-cann-kernels-910b_7.0.0_linux.run; Ascend-cann-toolkit_7.0.0_linux-aarch64.run; cmake=3.12.0, gcc=7.3.1 使用ascendspeed提供demo测试7b模型,模型初始化报错: [E OpParamMaker.cpp:286] call aclnnInplaceNormal failed, detail:EZ9999: Inner Error!
Fix support for fp16 kernels on nvidia 1080Ti(!571). Fix parsing of tuple type parameters (!316). Data processing Fix TypeErrors about can't pickle mindspore._c_dataengine.DEPipeline objects(!434). Add TFRecord file verification(!406). ...
it should re-adjust the computation based on that particular platform. This is exactly I think what you showcased with you Cuda enabled HPL-2.00 code. And this is from my own small experience trying to replicate the results from your work my self from scratch. Best regards, Michael...
so, it’s not surprising that it’s better, just surprising that it’s improved this much. Excited is an understatement. When I was testing some CUDA kernels against the CPU, I found a 8x speedup (compared to 100x, on my previous CPU). Want more numbers? On the same 4 drives, RAID...