A Beowulf cluster is a computer cluster of what are normally identical, commodity-grade computers networked into a small local area network with libraries and programs installed which allow processing to be shared among them. The result is a high-performance parallel computing cluster from inexpensive personal computer hardware.
NetApp, Inc. is an American hybrid cloud data services and data management company headquartered in Sunnyvale, California. It has ranked in the Fortune 500 since 2012. Founded in 1992 with an IPO in 1995, NetApp offers cloud data services for management of applications and data both online and physically.
General-purpose computing on graphics processing units is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU). The use of multiple video cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing.
GPFS is a high-performance clustered file system software developed by IBM. It can be deployed in shared-disk or shared-nothing distributed parallel modes, or a combination of these. It is used by many of the world's largest commercial companies, as well as some of the supercomputers on the Top 500 List. For example, it is the filesystem of the Summit at Oak Ridge National Laboratory which was the #1 fastest supercomputer in the world in the November 2019 top500 list of supercomputers. Summit is a 200 Petaflops system composed of more than 9,000 POWER9 processors and 27,000 NVIDIA Volta GPUs. The storage filesystem called Alpine has 250 PB of storage using Spectrum Scale on IBM ESS storage hardware, capable of approximately 2.5TB/s of sequential I/O and 2.2TB/s of random I/O.
VTune Profiler is a performance analysis tool for x86 based machines running Linux or Microsoft Windows operating systems. Many features work on both Intel and AMD hardware, but advanced hardware-based sampling requires an Intel-manufactured CPU.
A GPU cluster is a computer cluster in which each node is equipped with a Graphics Processing Unit (GPU). By harnessing the computational power of modern GPUs via General-Purpose Computing on Graphics Processing Units (GPGPU), very fast calculations can be performed with a GPU cluster.
Apache Hadoop is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model. Hadoop was originally designed for computer clusters built from commodity hardware, which is still the common use. It has since also found use on clusters of higher-end hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework.
CUDA is a parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing unit (GPU) for general purpose processing – an approach called general-purpose computing on GPUs (GPGPU). CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels.
A clustered file system is a file system which is shared by being simultaneously mounted on multiple servers. There are several approaches to clustering, most of which do not employ a clustered file system. Clustered file systems can provide features like location-independent addressing and redundancy which improve reliability or reduce the complexity of the other parts of the cluster. Parallel file systems are a type of clustered file system that spread data across multiple storage nodes, usually for redundancy or performance.
oVirt is a free, open-source virtualization management platform. It was founded by Red Hat as a community project on which Red Hat Virtualization is based. It allows centralized management of virtual machines, compute, storage and networking resources, from an easy-to-use web-based front-end with platform independent access. KVM on x86-64, PowerPC64 and s390x architecture are the only hypervisors supported, but there is an ongoing effort to support ARM architecture in a future releases.
Molecular modeling on GPU is the technique of using a graphics processing unit (GPU) for molecular simulations.
In computer science, memory virtualization decouples volatile random access memory (RAM) resources from individual systems in the data centre, and then aggregates those resources into a virtualized memory pool available to any computer in the cluster. The memory pool is accessed by the operating system or applications running on top of the operating system. The distributed memory pool can then be utilized as a high-speed cache, a messaging layer, or a large, shared memory resource for a CPU or a GPU application.
Linode, LLC is an American privately-owned cloud hosting company that provides virtual private servers. It is based in Philadelphia, Pennsylvania.
Microsoft RemoteFX is a Microsoft brand name that covers a set of technologies that enhance visual experience of the Microsoft-developed remote display protocol Remote Desktop Protocol (RDP). RemoteFX was first introduced in Windows Server 2008 R2 SP1 and is based on intellectual property that Microsoft acquired and continued to develop since acquiring Calista Technologies. It is a part of the overall Remote Desktop Services workload.
RenderScript is a component of the Android operating system for mobile devices that offers an API for acceleration that takes advantage of heterogeneous hardware. It allows developers to increase the performance of their applications at the cost of writing more complex (lower-level) code.
GPULib is a software library developed and marketed by Tech-X Corporation for accelerating general-purpose scientific computations from within the Interactive Data Language (IDL) using Nvidia's CUDA platform for programming its graphics processing units (GPUs). GPULib provides basic arithmetic, array indexing, special functions, Fast Fourier Transforms (FFT), interpolation, BLAS matrix operations as well as LAPACK routines provided by MAGMA, and some image processing operations. All numeric data types provided by IDL are supported. GPULib is used in medical imaging, optics, astronomy, earth science, remote sensing, and other scientific areas.
A distributed file system for cloud is a file system that allows many clients to have access to data and supports operations on that data. Each data file may be partitioned into several parts called chunks. Each chunk may be stored on different remote machines, facilitating the parallel execution of applications. Typically, data is stored in files in a hierarchical tree, where the nodes represent directories. There are several ways to share files in a distributed architecture: each solution must be suitable for a certain type of application, depending on how complex the application is. Meanwhile, the security of the system must be ensured. Confidentiality, availability and integrity are the main keys for a secure system.
GPU virtualization refers to technologies that allow the use of a GPU to accelerate graphics or GPGPU applications running on a virtual machine. GPU virtualization is used in various applications such as desktop virtualization, cloud gaming and computational science.
Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). It is a framework with wide support for deep learning algorithms. Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, word2vec, doc2vec, and GloVe. These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark.
oneAPI is an open standard for a unified application programming interface intended to be used across different compute accelerator (coprocessor) architectures, including GPUs, AI accelerators and field-programmable gate arrays. It is intended to eliminate the need for developers to maintain separate code bases, multiple programming languages, and different tools and workflows for each architecture.
