- BENTLEY MICROSTATION V8I NVIDIA DRIVER CONFIGURATION INSTALL
- BENTLEY MICROSTATION V8I NVIDIA DRIVER CONFIGURATION CODE
- BENTLEY MICROSTATION V8I NVIDIA DRIVER CONFIGURATION FREE
You could call cudaStreamSynchronize or even cudaThreadSynchronize instead but both are overkill in this case. As both events and the kernel are on the same stream (order is preserved) event1 and kernel got executed too. It's important to synchronize on event2 because you want to make sure everything got executed before calculating the time. Kernel>(.) //also using the default streamĬudaEventSynchronize(event2) //wait for the event to be executed!ĬudaEventElapsedTime(&dt_ms, event1, event2) Using events as timers basically comes down to this: //create eventsĬudaEventRecord(event1, 0) //where 0 is the default stream The programming guide has a detailed description what every one of those does. How your kernels use memory and how they are laid out on the GPU (in warps and blocks) will have a much more pronounced effect.Īctually there are even more synchronization functions ( cudaStreamSynchronize). There is no noticeable performance difference between the API's. Also, deviceĮmulation (see Section 4.5.2.9) does not work with the CUDA driver API. Instead of the execution configuration syntax described in Section 4.2.3. In particular, it is more difficult toĬonfigure and launch kernels using the CUDA driver API, since the executionĬonfiguration and kernel parameters must be specified with explicit function calls In contrast, the CUDA driver API requires more code, is harder to program andĭebug, but offers a better level of control and is language-independent since it onlyĭeals with cubin objects (see Section 4.2.5).
BENTLEY MICROSTATION V8I NVIDIA DRIVER CONFIGURATION CODE
Generated by nvcc is based on the CUDA runtime (see Section 4.2.5), soĪpplications that link to this code must use the CUDA runtime API.
Initialization, context management, and module management. The CUDA runtime eases device code management by providing implicit These APIs are mutually exclusive: An application should use either one or the
BENTLEY MICROSTATION V8I NVIDIA DRIVER CONFIGURATION FREE
For instance, the free memory available on the device can be queried only through the driver API. The programmer has to directly deal with initialization, module loading, etc.Īpparently more detailed device information can be queried through the driver API than through the runtime API. In contrast, the driver API is harder to program but provided more control over how CUDA is used. As you have noted, it is generally easier to use. This means that you don't have to distribute cubin files with your application, or deal with loading them through the driver API. The CUDA runtime makes it possible to compile and link your CUDA kernels into executables. The anwer to similar problems has been updating the nvidia display drivers, though in my case this is already done.
NVRM version: NVIDIA UNIX x86_64 Kernel Module 375.26 Thu Dec 8 18:36: ignDownloads/Enabled/MaterialDesignUserMa 96MiB | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. $ nvcc -version nvcc: NVIDIA (R) Cuda compiler driverĬopyright (c) 2005-2016 NVIDIA CorporationĬuda compilation tools, release 8.0, V8.0.44 > CUDA driver version is insufficient for CUDA runtime version deviceQueryĬUDA Device Query (Runtime API) version (CUDART static linking)
Running the deviceQuery in CUDA samples produces the following error however: ~/cudasamples/NVIDIA_CUDA-8.0_Samples/1_Utilities/deviceQuery$.
BENTLEY MICROSTATION V8I NVIDIA DRIVER CONFIGURATION INSTALL
I've installed the latest nvidia drivers (375.26) manually, and installed CUDA using cuda_8.0.44_n (skipping the driver install there, since the bundled drivers are older, 367 I think).