Search Results for "compute"

Karpenter simplifies Kubernetes infrastructure with the right nodes at the right time. Karpenter automatically launches just the right compute resources to handle your cluster's applications. It is designed to let you take full advantage of the cloud with fast and simple compute provisioning for Kubernetes clusters. Karpenter responds quickly and automatically to changes in application load, scheduling, and resource requirements, placing new workloads on a variety of available compute resource capacities. ...

...AWS ParallelCluster is an AWS-supported Open Source cluster management tool that makes it easy for you to deploy and manage High-Performance Computing (HPC) clusters in the AWS cloud. Built on the Open Source CfnCluster project, AWS ParallelCluster enables you to quickly build an HPC compute environment in AWS. It automatically sets up the required compute resources and a shared filesystem and offers a variety of batch schedulers such as AWS Batch and Slurm. AWS ParallelCluster facilitates both quick start proof of concepts (POCs) and production deployments. You can build higher-level workflows, such as a Genomics portal that automates the entire DNA sequencing workflow, on top of AWS ParallelCluster.

...You can run Pelican on a single multiple core machine to use all cores to solve a problem, or you can network multiple computers together to make a cluster. The frontend node (either a real computer or a virtual machine) boots from the image. The compute nodes boot by PXE, using the frontend node as the server. All of the nodes of the cluster get their filesystems from the same image, so it is guaranteed that all nodes run the the same software. Packages can be added to all nodes using apt-get, thanks to aufs. The bootable image is created by running a single script, which takes advantage of the Debian Live infrastructure.

dispy is a generic and comprehensive, yet easy to use framework for creating and using compute clusters to execute computations in parallel across multiple processors in a single machine (SMP), among many machines in a cluster, grid or cloud. dispy is well suited for data parallel (SIMD) paradigm where a computation (Python function or standalone program) is evaluated with different (large) datasets independently. dispy supports public / private / hybrid cloud computing, fog / edge computing.

...Download pages, repositories, tickets and wiki have been moved to https://github.com/oss-tsukuba/ Download: gfarm: https://github.com/oss-tsukuba/gfarm/releases gfarm2fs: https://github.com/oss-tsukuba/gfarm2fs/releases and more: https://github.com/oss-tsukuba/gfarm/wiki#download Repositories: gfarm: https://github.com/oss-tsukuba/gfarm gfarm2fs: https://github.com/oss-tsukuba/gfarm2fs and more: https://github.com/orgs/oss-tsukuba/repositories Tickets: gfarm: https://github.com/oss-tsukuba/gfarm/issues gfarm2fs: https://github.com/oss-tsukuba/gfarm2fs/issues Wiki: https://github.com/oss-tsukuba/gfarm/wiki Gfarm file system is a network shared file system that supports scalable I/O performance in distributed environment. It can federate local disks of network-connected PCs and compute nodes in several clusters.

Manage node sets, node groups and execute commands on cluster nodes in parallel. Provides an event-based Python library to improve administration of large compute clusters or server farms. Command line tools: clush and nodeset included.

This roll provides rpm packages to mount the lustre filesystem on the frontends and compute nodes of a rocks cluster. Please contact me if you need this roll for another kernel and/or lustre version.

ThunderstormDistributor is a queuing system that distributes jobs and computational workload across dynamic clusters in the cloud. It manages the assignment of jobs to maximize CPU and memory usage and prevent oversubscription of compute nodes. It also performs advanced statistics collection on individual compute nodes and jobs to graph the distribution of disk, network, CPU, and memory usage over time, which facilitates the advanced optimization and tuning of computational workflows.

RainforestCluster is an Amazon EC2 python program that manages and load-balances dynamic clusters to allow for maximum workflow flexibility and speed at minimal cost. It enables one to quickly and cheaply create dynamic compute clusters in the cloud, which can then run computational pipelines generically. It is also able to optimize the use of spot instances - idle computers in Amazon's cloud that are available at drastically reduced cost (5x-10x cheaper) - but can be terminated at any moment if capacity drops or the bid price rises. It also provides pre-installed features such as GlusterFS distributed filesystems, ThunderstormDistributor queuing system, RAID 0 /scratch, password-less ssh, and automatic cluster management, for ease of use and maximum processing speed for computational tools. ...

Cbench is intended as a relatively straightforward toolbox of tests, benchmarks, applications, utilities, and framework to hold them together with the goal to facilitate scalable testing, benchmarking, and analysis of a Linux parallel compute cluster.

Tuda-rsync_install is a initrd image containing scripts to partition and rsync a rootfs to compute, or other nodes. A fast, easy and flexible solution not only for HPC systems, although it has been developed for that purpose.

SCR caches checkpoint data in storage on the compute nodes of a Linux cluster to provide a fast, scalable checkpoint / restart capability for MPI codes.

PPIV is a parallel software application used in Particle Image Velocimetry (PIV) analysis. The software will compute the vector field for one image pair or multiple image pairs. PPIV can run on a single desktop machine or in a cluster environment.

Cbench is intended to be a relatively straightforward collection of tests, benchmarks, applications, utilities, and framework to hold them together with the goal to facilitate scalable testing and benchmarking of a Linux parallel compute cluster.

Designed to allow quick setup of powerful NOW-based clusters to run compute-intensive applications. Applications do not have to be designed specially for these clusters to benefit.