A powerful new open source deep learning framework for drug discovery is now available for public download on github. This new framework, called DeepChem, is python-based, and offers a feature-rich set of functionality for applying deep learning to problems in drug discovery and cheminformatics. Previous deep learning frameworks, such as scikit-learn have been applied to chemiformatics, but DeepChem is the first to accelerate computation with NVIDIA GPUs.
The framework uses Google TensorFlow, along with scikit-learn, for expressing neural networks for deep learning. It also makes use of the RDKit python framework, for performing more basic operations on molecular data, such as converting SMILES strings into molecular graphs. The framework is now in the alpha stage, at version 0.1. As the framework develops, it will move toward implementing more models in TensorFlow, which use GPUs for training and inference. This new open source framework is poised to become an accelerating factor for innovation in drug discovery across industry and academia.
Fighting with application installations is frustrating and time consuming. It’s not what domain experts should be spending their time on. And yet, every time users move their project to a new system, they have to begin again with a re-assembly of their complex workflow.
This is a problem that containers can help to solve. HPC groups have had some success with more traditional containers (e.g., Docker), but there are security concerns that have made them difficult to use on HPC systems. Singularity, the new tool from the creator of CentOS and Warewulf, aims to resolve these issues.
As NVIDIA’s GPUs become increasingly vital to the fields of AI and intelligent machines, NVIDIA has produced GPU models specifically targeted to these applications. The new Tesla P40 GPU is NVIDIA’s premiere product for deep learning deployments. It is specifically designed for high-speed inference workloads, which means running data through pre-trained neural networks. However, it also offers significant processing performance for projects which do not require 64-bit double-precision floating point capability (many neural networks can be trained using the 32-bit single-precision floating point on the Tesla P40). For those cases, these GPUs can be used to accelerate both the neural network training and the inference.
Sources of CPU benchmarks, used for estimating performance on similar workloads, have been available throughout the course of CPU development. For example, the Standard Performance Evaluation Corporation has compiled a large set of applications benchmarks, running on a variety of CPUs, across a multitude of systems. There are certainly benchmarks for GPUs, but only during the past year has an organized set of deep learning benchmarks been published. Called DeepMarks, these deep learning benchmarks are available to all developers who want to get a sense of how their application might perform across various deep learning frameworks.
The benchmarking scripts used for the DeepMarks study are published at GitHub. The original DeepMarks study was run on a Titan X GPU (Maxwell microarchitecture), having 12GB of onboard video memory. Here we will examine the performance of several deep learning frameworks on a variety of Tesla GPUs, including the Tesla P100 16GB PCIe, Tesla K80, and Tesla M40 12GB GPUs.
The new NVIDIA Tesla P100 GPUs are available with both PCI-Express and NVLink connectivity. How do these two types of connectivity compare? This post provides a rundown of NVLink vs PCI-E and explores the benefits of NVIDIA’s new NVLink technology.
The NVIDIA Tesla P100 NVLink GPUs are a big advancement. For the first time, the GPU is stepping outside the traditional “add in card” design. No longer tied to the fixed specifications of PCI-Express cards, NVIDIA’s engineers have designed a new form factor that best suits the needs of the GPU. With their SXM2 design, NVIDIA can run GPUs to their full potential.
One of the biggest changes this allows is the NVLink interconnect, which allows GPUs to operate beyond the restrictions of the PCI-Express bus. Instead, the GPUs communicate with one another over this high-speed link. Additionally, these new “Pascal” architecture GPUs bring improvements including higher performance, faster connectivity, and more flexibility for users & programmers.
NVIDIA’s new Tesla P100 PCI-E GPU is a big step up for HPC users, and for GPU users in general. Although other workloads have been leveraging the newer “Maxwell” architecture, HPC applications have been using “Kepler” GPUs for a couple years. The new GPUs bring many improvements, including higher performance, faster connectivity, and more flexibility for users & programmers.
Now that NVIDIA has launched their new Pascal GPUs, the next question is “What is the Tesla P100 Price?”
Although it’s still a month or two before shipments of P100 start, the specifications and pricing of Microway’s Tesla P100 GPU-accelerated systems are available. If you’re planning a new project for delivery later this year, we’d be happy to help you get on board. These new GPUs are exceptionally powerful.
One blog post may not be enough to present all tips for performance acceleration using OpenACC. So here, more tips on OpenACC acceleration are provided, complementing our previous blog post on accelerating code with OpenACC.
Further tips discussed here are:
- linearizing a 2D array
- usage of contiguous memory
- parallelizing loops
- PGI compiler information reports
- OpenACC general guidelines
- the OpenACC runtime library
If you’ve been reading the press this year, you’ve probably seen mention of deep learning or machine learning. You’ve probably gotten the impression they can do anything and solve every problem. It’s true that computers can be better than humans at recognizing people’s faces or playing the game Go. However, it’s not the solution to every problem. We want to help you understand if you can use deep learning. And if so, how it will help you.