One of the main reasons for accelerating code on an NVIDIA GPU is for an increase in application performance. This is why it’s important to use the best tools available to help you get the performance you’re looking for. CUDA 6 includes great improvements to the guided analysis tool in the NVIDIA Visual Profiler. Watch today’s CUDACast to see how to use guided analysis to locate potential optimizations for your GPU code.
You can find the code used in this video in the CUDACasts GitHub repository.
Today I’m excited to announce the release of CUDA 6, a new version of the CUDA Toolkit that includes some of the most significant new functionality in the history of CUDA. In this brief post I will share with you the most important new features in CUDA 6 and tell you where to get more information. You may also want to watch the recording of my talk “CUDA 6 and Beyond” from last month’s GPU Technology Conference, embedded below.
Without further ado, if you are ready to download the CUDA Toolkit version 6.0 now, by all means, go get it on CUDA Zone. The five most important new features of CUDA 6 are
- support for Unified Memory;
- CUDA on Tegra K1 mobile/embedded system-on-a-chip;
- XT and Drop-In library interfaces;
- remote development in NSight Eclipse Edition;
- many improvements to the CUDA developer tools.
CUDA 6 introduces Unified Memory, which dramatically simplifies memory management for GPU computing. Now you can focus on writing parallel kernels when porting code to the GPU, and memory management becomes an optimization.
The CUDA 6 Release Candidate is now publicly available. In today’s CUDACast, I will show you some simple examples showing how easy it is to accelerate code on the GPU using Unified Memory in CUDA 6, and how powerful Unified Memory is for sharing C++ data structures between host and device code. If you’re interested in looking at the code in detail, you can find it in the Parallel Forall repository on GitHub. You can also check out the great Unified Memory post by Mark Harris.
Alex St. John has a new post on his blog “The Saint” about his first experience porting C++ classes to run on the GPU with CUDA 6 and Unified Memory.
The introduction of Unified Memory in CUDA, for the first time makes it practical to move huge bodies of general C++ code entirely up to the GPU and to write and run entire complex code systems entirely on the GPU with minimal CPU governance. In theory a big leap, but not without some new challenges.
Alex extends the example I provided in my post Unified Memory in CUDA 6 to make it portable between the CPU, with a switch to select managed memory or host memory allocation. He also touches on an approach to making the member functions of the class portable (using
__host__ __device__—see my post about Hemi for more ideas on this topic).
Overall it looks like Alex had a very positive experience with Unified Memory: “Using this approach I ported several thousand lines of C++ code and half a dozen objects to CUDA 6.0 in a couple days.” I expect many programmers to have similar good experiences in the future.
With CUDA 6, we’re introducing one of the most dramatic programming model improvements in the history of the CUDA platform, Unified Memory. In a typical PC or cluster node today, the memories of the CPU and GPU are physically distinct and separated by the PCI-Express bus. Before CUDA 6, that is exactly how the programmer has to view things. Data that is shared between the CPU and GPU must be allocated in both memories, and explicitly copied between them by the program. This adds a lot of complexity to CUDA programs.
Unified Memory creates a pool of managed memory that is shared between the CPU and GPU, bridging the CPU-GPU divide. Managed memory is accessible to both the CPU and GPU using a single pointer. The key is that the system automatically migrates data allocated in Unified Memory between host and device so that it looks like CPU memory to code running on the CPU, and like GPU memory to code running on the GPU.
In this post I’ll show you how Unified Memory dramatically simplifies memory management in GPU-accelerated applications. The image below shows a really simple example. Both codes load a file from disk, sort the bytes in it, and then use the sorted data on the CPU, before freeing the memory. The code on the right runs on the GPU using CUDA and Unified Memory. The only differences are that the GPU version launches a kernel (and synchronizes after launching it), and allocates space for the loaded file in Unified Memory using the new API
If you have programmed CUDA C/C++ before, you will no doubt be struck by the simplicity of the code on the right. Notice that we allocate memory once, and we have a single pointer to the data that is accessible from both the host and the device. We can read directly into the allocation from a file, and then we can pass the pointer directly to a CUDA kernel that runs on the device. Then, after waiting for the kernel to finish, we can access the data again from the CPU. The CUDA runtime hides all the complexity, automatically migrating data to the place where it is accessed. Continue reading