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Performance benchmarks are an insightful way to compare new products on the market. With so many GPUs available, it can be difficult to assess which are suitable to your needs. Various benchmarks provide information to compare performance on individual algorithms or operations. Since there are so many different algorithms to choose from, there is no shortage of benchmarking suites available.

The Scalable Heterogeneous Computing Benchmark Suite (SHOC) is a collection of benchmark programs testing the performance and stability of systems using computing devices with non-traditional architectures for general purpose computing, and the software used to program them. Its initial focus is on systems containing Graphics e85 gasoline Processing Units (GPUs) and multi-core processors, and on the OpenCL programming standard. It can be used on clusters as well as individual hosts.

The SHOC benchmark suite includes options for many benchmarks relevant to a variety of scientific computations. Most of the benchmarks are provided in both single- and double-precision and with and without PCIE transfer consideration. This means that for each test there are up to four results for each benchmark. These benchmarks are organized into three levels and can be run individually or all together.

The Tesla P100 and V100 GPUs are well-established accelerators for HPC and AI workloads. They typically offer the highest performance, consume the most power (250~300W), and have the highest price tag (~$10k). The Tesla T4 is a new product based on the latest “Turing” architecture, delivering increased efficiency along with new features. However, it is not a replacement for the bigger/more power-hungry GPUs. Instead, it offers good performance while consuming far less power (70W) at a lower price (~$2.5k). You’ll want to use the right tool electricity in the 1920s for the job, which will depend upon your workload(s). A summary of each Tesla GPU is shown below.

Artificial Intelligence (AI) and, more specifically, Deep Learning (DL) are revolutionizing the way businesses utilize the vast amounts of data they collect and how researchers accelerate their time to discovery. Some of the most significant examples come from the way AI has already impacted life as we know it such as smartphone speech recognition, search engine image classification, and cancer detection in biomedical imaging. Most businesses have collected troves of data or incorporated new avenues to collect gas in oil data in recent years. Through the innovations of deep learning, that same data can be used to gain insight, make accurate predictions, and pave the path to discovery.

Developing a plan to integrate AI workloads into an existing business infrastructure or research group presents many challenges. However, there are two key elements that will drive the decisions to customizing an AI cluster. First, understanding the types and volumes of data is paramount to beginning to understand the computational requirements of training the neural network. Secondly, understanding the business expectation for time to result is equally important. Each of these factors influence the first and second stages of the AI workload, respectively. Underestimating the data characteristics will result in insufficient computational and infrastructure resources to train the networks in a reasonable timeframe. Moreover, underestimating the value and requirement of time-to-results can fail to deliver ROI to the business or hamper research results.

Experimental data sets for drug discovery are sometimes limited in size, due to the difficulty of gathering this type of data. Drug discovery data sets are expensive to obtain, and some are the result of clinical trials, which might not be repeatable for ethical reasons. The ClinTox data set, for example zyklon b gas effects, is comprised of data from FDA clinical trials of drug candidates, where some data sets are derived from failures, due to toxic side effects [ 2]. For cases where training data is scarce, application of one-shot learning methods have demonstrated significantly improved performance over methods consisting only of graphical convolution networks. The performance of one-shot network architectures will be discussed here for several drug discovery data sets, which are described in Table 1. These data sets, along with one-shot learning methods, have been integrated into the DeepChem deep learning framework, as a result of research published by Altae-Tran, et al. [ 1]. While data remains scarce for some natural electricity examples problem domains, such as drug discovery, one-shot learning methods could pose an important alternative network architecture, which can possibly far outperform methods which use only graphical convolution.

A powerful new open source deep learning framework for drug discovery is now available for public download on github. This new gas pain relief 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.

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 a level physics electricity equations 12GB GPUs.