Cuda driver api example

There is a devicequerydrvapi example in the cuda samples included with the cuda sdk and starting with cuda 5. It demonstrates how to link to cuda driver at runtime and how to use jit justintime compilation from ptx code. This context can be used by subsequent driver api calls. This package makes it possible to interact with cuda hardware through userfriendly wrappers of cudas driver api. For further details on cuda contexts, refer to the cuda driver api documentation on context management and the cuda c programming guide context documentation. Instead, the jcuda driver api has to be used, as explained in the section about creating kernels. An even easier introduction to cuda nvidia developer blog.

Opengl is a graphics library used for 2d and 3d rendering. Clojurecuda a clojure library for parallel computations. 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. This article shows the fundamentals of using cuda for accelerating convolution operations. Does dynamic parallelism even work with the cuda driver api. Thus, for example, the function may always use memory attached to. For microsoft platforms, nvidias cuda driver supports directx. But cuda programming has gotten easier, and gpus have gotten much faster, so its time for an updated and even easier introduction. 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.

As i remember nvidia obsoletes some old cuda hw with each new driver release. What you shouldnt do is mix both as in your first example. Get started the above options provide the complete cuda toolkit for application development. Cuda driver api, vector addition, runtime compilation supported sm architectures sm 3. Each cuda device in a system has an associated cuda context, and numba presently allows only one context per thread. Can dynamic parallelism work when the device code containing parent and child kernels is compiled to ptx and then linked. The jit decorator is applied to python functions written in our python dialect for cuda.

The examples ive seen have all the code cpu and device in a. 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. For example, the driver api api contains cueventcreate while the runtime api contains cudaeventcreate, with similar functionality. 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. Nvcc and hcc target different architectures and use different code object formats. Different streams may execute their commands concurrently or out of order with respect to each other.

This crate provides a safe, userfriendly wrapper around the cuda driver api. 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. This shows you how to query what you need with the driver api. Cuda python functions execute within a cuda context. 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. Matrix multiplication cuda driver api version this sample implements matrix multiplication and uses the new cuda 4. Closely follows cuda driver api you can easily translate examples from best books about cuda. Cuda provides both a low level api cuda driver api, non singlesource and a higher level api cuda runtime api, singlesource. Cuda driver api university of california, san diego.

Vector addition example using cuda driver api github. This sample revisits matrix multiplication using the cuda driver api. Accelerating convolution operations by gpu cuda, part 1. 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. It translates python functions into ptx code which execute on the cuda hardware. Alternatively, you can use the driver api to initiate the context. 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.

Nvcc is cubin or ptx files, while the hcc path is the hsaco format. Consequently, we highly recommend that this book be used in conjunction with nvidias freely available documentation, in. Simple techniques demonstrating basic approaches to gpu computing best practices for the most important features working efficiently with custom data types quickly. The jcuda runtime api is mainly intended for the interaction with the java bindings of the the.

Discover latest cuda capabilities learn about the latest features in cuda toolkit including updates to the programming model, computing libraries and development tools. 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. Demonstrates a matrix multiplication using shared memory through tiled approach, uses cuda driver api. Runtime components for deploying cudabased applications are available in readyto. With cuda, developers are able to dramatically speed up computing applications by harnessing the power of gpus. Matrix multiplication driver version this sample implements matrix multiplication using the cuda driver api. The cuda jit is a lowlevel entry point to the cuda features in numbapro. Cuda has also been used to accelerate nongraphical applications in computational biology, cryptography and other fields by an order of magnitude or more. This cuda driver api sample uses nvrtc for runtime compilation of vector. This post is a super simple introduction to cuda, the popular parallel computing platform and programming model from nvidia. This section describes the interactions between the cuda driver api and the cuda runtime api. You can use its source code as a realworld example of how to harness gpu power from clojure. I wrote a previous easy introduction to cuda in 20 that has been very popular over the years.