Cuda driver api example

Matrix multiplication driver version this sample implements matrix multiplication using the cuda driver api. Demonstrates a gemm computation using the warp matrix multiply and accumulate wmma api introduced in cuda 9, as well as the new tensor cores introduced in the volta chip family. Consequently, we highly recommend that this book be used in conjunction with nvidias freely available documentation, in. Numbapro interacts with the cuda driver api to load the ptx onto the cuda device and execute. For further details on cuda contexts, refer to the cuda driver api documentation on context management and the cuda c programming guide context documentation. Vector addition example using cuda driver api github. It never does any explicit context management itself, makes no attempt to do anything related to interoperability with the driver api, and the handle contains no context. Clojurecuda a clojure library for parallel computations. Nvcc is cubin or ptx files, while the hcc path is the hsaco format. Does dynamic parallelism even work with the cuda driver api. This context can be used by subsequent driver api calls. This cuda driver api sample uses nvrtc for runtime compilation of vector.

Demonstrates a matrix multiplication using shared memory through tiled approach, uses cuda driver api. Since convolution is the important ingredient of many applications such as convolutional neural networks and image processing, i hope this article on cuda would help you. Afaik, cublas the example library in question is a completely plain runtime api library which relies entirely on standard runtime api lazy context management behaviour. Thus, it is not possible to call own cuda kernels with the jcuda runtime api. Each cuda device in a system has an associated cuda context, and numba presently allows only one context per thread. There is a devicequerydrvapi example in the cuda samples included with the cuda sdk and starting with cuda 5. Since the highlevel api is implemented above the lowlevel api, each call to a function of the runtime is broken down into more basic instructions. It has been written for clarity of exposition to illustrate various cuda programming principles, not with the goal of providing the most performant generic kernel for matrix multiplication. Cuda provides both a low level api cuda driver api, non singlesource and a higher level api cuda runtime api, singlesource. Nvcc and hcc target different architectures and use different code object formats. This shows you how to query what you need with the driver api. The cuda jit is a lowlevel entry point to the cuda features in numbapro.

Alternatively, you can use the driver api to initiate the context. This package makes it possible to interact with cuda hardware through userfriendly wrappers of cudas driver api. You can use its source code as a realworld example of how to harness gpu power from clojure. Runtime components for deploying cudabased applications are available in readyto.

Matrix multiplication cuda driver api version this sample implements matrix multiplication and uses the new cuda 4. It translates python functions into ptx code which execute on the cuda hardware. Few cuda samples for windows demonstrates cuda directx12 interoperability, for building such samples one needs to install windows 10 sdk or higher, with vs 2015 or vs 2017. Learn cuda through getting started resources including videos, webinars, code examples and handson labs. With cuda, developers are able to dramatically speed up computing applications by harnessing the power of gpus. Opengl is a graphics library used for 2d and 3d rendering. This sample revisits matrix multiplication using the cuda driver api. It demonstrates how to link to cuda driver at runtime and how to use jit justintime compilation from ptx code. Discover latest cuda capabilities learn about the latest features in cuda toolkit including updates to the programming model, computing libraries and development tools.

It can cause trouble for users writing plugins for larger software packages, for example, because if all plugins run in the same process, they will. This article shows the fundamentals of using cuda for accelerating convolution operations. For example, the driver api api contains cueventcreate while the runtime api contains cudaeventcreate, with similar functionality. It is meant to form a strong foundation for all interactions.

Can dynamic parallelism work when the device code containing parent and child kernels is compiled to ptx and then linked. This section describes the interactions between the cuda driver api and the cuda runtime api. Objects in driver api object handle description device cudevice cudaenabled device context cucontext roughly equivalent to a cpu process module cumodule roughly equivalent to a dynamic library function cufunction kernel heap memory cudeviceptr pointer to device memory cuda array cuarray opaque container for onedimensional or twodimensional. The jcuda runtime api is mainly intended for the interaction with the java bindings of the the. I wrote a previous easy introduction to cuda in 20 that has been very popular over the years. The examples ive seen have all the code cpu and device in a. Cuda driver api university of california, san diego. What you shouldnt do is mix both as in your first example.

Thus, for example, the function may always use memory attached to. Cuda driver api, vector addition, runtime compilation supported sm architectures sm 3. Simple techniques demonstrating basic approaches to gpu computing best practices for the most important features working efficiently with custom data types quickly. Kernel code example matrix mulplicaon kernel in c for cuda and opencl c see the handout host api usage compared c runme for cuda cuda driver api opencl api setup inialize driver get devices. But cuda programming has gotten easier, and gpus have gotten much faster, so its time for an updated and even easier introduction. This crate provides a safe, userfriendly wrapper around the cuda driver api. Accelerating convolution operations by gpu cuda, part 1. Get started the above options provide the complete cuda toolkit for application development. This post is a super simple introduction to cuda, the popular parallel computing platform and programming model from nvidia. Cuda python functions execute within a cuda context. The jit decorator is applied to python functions written in our python dialect for cuda. An even easier introduction to cuda nvidia developer blog. Cuda has also been used to accelerate nongraphical applications in computational biology, cryptography and other fields by an order of magnitude or more. For microsoft platforms, nvidias cuda driver supports directx.