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Mellanox Technologies Announces the Appointment of Dave Sheffler As Vice President of Worldwide Sales
MELLANOX TECHNOLOGIES ANNOUNCES THE APPOINTMENT OF DAVE SHEFFLER AS VICE PRESIDENT OF WORLDWIDE SALES Former VP of AMD joins InfiniBand Internet Infrastructure Startup SANTA CLARA, CA – January 17, 2000 – Mellanox Technologies, a fabless semiconductor startup company developing InfiniBand semiconductors for the Internet infrastructure, today announced the appointment of Dave Sheffler as its Vice President of Worldwide Sales, reporting to Eyal Waldman, Chief Executive Officer and Chairman of the Board. “Dave Sheffler possesses the leadership, business capabilities, and experience which complement the formidable engineering, marketing, and operations talent that Mellanox has assembled. He combines the highest standards of ethics and achievements, with an outstanding record of grow- ing sales in highly competitive and technical markets. The addition of Dave to the team brings an additional experienced business perspective and will enable Mellanox to develop its sales organi- zation and provide its customers the highest level of technical and logistical support,” said Eyal Waldman. Prior to joining Mellanox Dave served as Vice President of Sales and Marketing for the Americas for Advanced Micro Devices (NYSE: AMD). Previously, Mr. Sheffler was the VP of Worldwide Sales for Nexgen Inc., where he was part of the senior management team that guided the company’s growth, leading to a successful IPO and eventual sale to AMD in January 1996. Mellanox Technologies Inc. 2900 Stender Way, Santa Clara, CA 95054 Tel: 408-970-3400 Fax: 408-970-3403 www.mellanox.com 1 Mellanox Technologies Inc MELLANOX TECHNOLOGIES ANNOUNCES THE APPOINTMENT OF DAVE SHEFFLER AS VICE PRESIDENT OF WORLDWIDE Dave’s track record demonstrates that he will be able to build a sales organization of the highest caliber. -
IBM Cloud Web
IBM’s iDataPlex with Mellanox® ConnectX® InfiniBand Adapters Dual-Port ConnectX 20 and 40Gb/s InfiniBand PCIe Adapters Data center cost structures are shifting, from equipment-focused to space-, power- and personnel-focused. Total cost of owner- ship and green initiatives are major spending drivers as data centers are being refreshed. Topping off those challenges is the rapid evolution of information technology becoming a competitive advantage through business process management and deployment of service oriented architectures. I/O technology plays a key role in meeting many of the spending drivers – provisioning capacity for future growth, efficient scaling of compute, LAN and SAN capacity, reduction of space and power in the data center helping reduce TCO and doing so while enhancing data center agility. IBM iDataPlex and ConnectX InfiniBand Adapter Cards IBM’s iDataPlex with Mellanox ConnectX InfiniBand adapters High-Performance for the Flexible Data Center offers a solution that is eco-friendly, flexible, responsive, and Scientists, engineers, and analysts eager to solve challenging positioned to address the evolving requirements of Internet-Scale problems in engineering, financial markets, and the life and earth and High-Performance sciences rely on high-performance systems. In addition, with the use of multi-core CPUs, virtualized infrastructures and networked Unprecedented Performance, Scalability, and storage, users are demanding increased efficiency of the entire Efficiency for Cloud Computing IT compute and storage infrastructure. -
MLNX OFED Documentation Rev 5.0-2.1.8.0
MLNX_OFED Documentation Rev 5.0-2.1.8.0 Exported on May/21/2020 06:13 AM https://docs.mellanox.com/x/JLV-AQ Notice This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. NVIDIA Corporation (“NVIDIA”) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality. NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice. Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. NVIDIA products are sold subject to the NVIDIA standard terms and conditions of sale supplied at the time of order acknowledgement, unless otherwise agreed in an individual sales agreement signed by authorized representatives of NVIDIA and customer (“Terms of Sale”). NVIDIA hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. No contractual obligations are formed either directly or indirectly by this document. NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. -
Optimizing the Ceph Distributed File System for High Performance Computing
Optimizing the Ceph Distributed File System for High Performance Computing Kisik Jeong Carl Duffy Jin-Soo Kim Joonwon Lee Sungkyunkwan University Seoul National University Seoul National University Sungkyunkwan University Suwon, South Korea Seoul, South Korea Seoul, South Korea Suwon, South Korea [email protected] [email protected] [email protected] [email protected] Abstract—With increasing demand for running big data ana- ditional HPC workloads. Traditional HPC workloads perform lytics and machine learning workloads with diverse data types, reads from a large shared file stored in a file system, followed high performance computing (HPC) systems consequently need by writes to the file system in a parallel manner. In this case, to support diverse types of storage services. Ceph is one possible candidate for such HPC environments, as Ceph provides inter- a storage service should provide good sequential read/write faces for object, block, and file storage. Ceph, however, is not performance. However, Ceph divides large files into a number designed for HPC environments, thus it needs to be optimized of chunks and distributes them to several different disks. This for HPC workloads. In this paper, we find and analyze problems feature has two main problems in HPC environments: 1) Ceph that arise when running HPC workloads on Ceph, and propose translates sequential accesses into random accesses, 2) Ceph a novel optimization technique called F2FS-split, based on the F2FS file system and several other optimizations. We measure needs to manage many files, incurring high journal overheads the performance of Ceph in HPC environments, and show that in the underlying file system. -
Proxmox Ve Mit Ceph &
PROXMOX VE MIT CEPH & ZFS ZUKUNFTSSICHERE INFRASTRUKTUR IM RECHENZENTRUM Alwin Antreich Proxmox Server Solutions GmbH FrOSCon 14 | 10. August 2019 Alwin Antreich Software Entwickler @ Proxmox 15 Jahre in der IT als Willkommen! System / Netzwerk Administrator FrOSCon 14 | 10.08.2019 2/33 Proxmox Server Solutions GmbH Aktive Community Proxmox seit 2005 Globales Partnernetz in Wien (AT) Proxmox Mail Gateway Enterprise (AGPL,v3) Proxmox VE (AGPL,v3) Support & Services FrOSCon 14 | 10.08.2019 3/33 Traditionelle Infrastruktur FrOSCon 14 | 10.08.2019 4/33 Hyperkonvergenz FrOSCon 14 | 10.08.2019 5/33 Hyperkonvergente Infrastruktur FrOSCon 14 | 10.08.2019 6/33 Voraussetzung für Hyperkonvergenz CPU / RAM / Netzwerk / Storage Verwende immer genug von allem. FrOSCon 14 | 10.08.2019 7/33 FrOSCon 14 | 10.08.2019 8/33 Was ist ‚das‘? ● Ceph & ZFS - software-basierte Storagelösungen ● ZFS lokal / Ceph verteilt im Netzwerk ● Hervorragende Performance, Verfügbarkeit und https://ceph.io/ Skalierbarkeit ● Verwaltung und Überwachung mit Proxmox VE ● Technischer Support für Ceph & ZFS inkludiert in Proxmox Subskription http://open-zfs.org/ FrOSCon 14 | 10.08.2019 9/33 FrOSCon 14 | 10.08.2019 10/33 FrOSCon 14 | 10.08.2019 11/33 ZFS Architektur FrOSCon 14 | 10.08.2019 12/33 ZFS ARC, L2ARC and ZIL With ZIL Without ZIL ARC RAM ARC RAM ZIL ZIL Application HDD Application SSD HDD FrOSCon 14 | 10.08.2019 13/33 FrOSCon 14 | 10.08.2019 14/33 FrOSCon 14 | 10.08.2019 15/33 Ceph Network Ceph Docs: https://docs.ceph.com/docs/master/ FrOSCon 14 | 10.08.2019 16/33 FrOSCon -
Red Hat Openstack* Platform with Red Hat Ceph* Storage
Intel® Data Center Blocks for Cloud – Red Hat* OpenStack* Platform with Red Hat Ceph* Storage Reference Architecture Guide for deploying a private cloud based on Red Hat OpenStack* Platform with Red Hat Ceph Storage using Intel® Server Products. Rev 1.0 January 2017 Intel® Server Products and Solutions <Blank page> Intel® Data Center Blocks for Cloud – Red Hat* OpenStack* Platform with Red Hat Ceph* Storage Document Revision History Date Revision Changes January 2017 1.0 Initial release. 3 Intel® Data Center Blocks for Cloud – Red Hat® OpenStack® Platform with Red Hat Ceph Storage Disclaimers Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software, or service activation. Learn more at Intel.com, or from the OEM or retailer. You may not use or facilitate the use of this document in connection with any infringement or other legal analysis concerning Intel products described herein. You agree to grant Intel a non-exclusive, royalty-free license to any patent claim thereafter drafted which includes subject matter disclosed herein. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document. The products described may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request. Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade. Copies of documents which have an order number and are referenced in this document may be obtained by calling 1-800-548-4725 or by visiting www.intel.com/design/literature.htm. -
Red Hat Data Analytics Infrastructure Solution
TECHNOLOGY DETAIL RED HAT DATA ANALYTICS INFRASTRUCTURE SOLUTION TABLE OF CONTENTS 1 INTRODUCTION ................................................................................................................ 2 2 RED HAT DATA ANALYTICS INFRASTRUCTURE SOLUTION ..................................... 2 2.1 The evolution of analytics infrastructure ....................................................................................... 3 Give data scientists and data 2.2 Benefits of a shared data repository on Red Hat Ceph Storage .............................................. 3 analytics teams access to their own clusters without the unnec- 2.3 Solution components ...........................................................................................................................4 essary cost and complexity of 3 TESTING ENVIRONMENT OVERVIEW ............................................................................ 4 duplicating Hadoop Distributed File System (HDFS) datasets. 4 RELATIVE COST AND PERFORMANCE COMPARISON ................................................ 6 4.1 Findings summary ................................................................................................................................. 6 Rapidly deploy and decom- 4.2 Workload details .................................................................................................................................... 7 mission analytics clusters on 4.3 24-hour ingest ........................................................................................................................................8 -
Meridian Contrarian Fund Holdings As of 12/31/2016
Meridian Contrarian Fund Holdings as of 12/31/2016 Ticker Security Name % Allocation NVDA NVIDIA CORP 5.8% MSFT MICROSOFT CORP 4.1% CFG CITIZENS FINANCIAL GROUP INC 3.8% ALEX ALEXANDER & BALDWIN INC 3.5% EOG EOG RESOURCES INC 3.3% CACI CACI INTERNATIONAL INC 3.3% USB US BANCORP 3.1% XYL XYLEM INC/NY 2.7% TOT TOTAL SA 2.6% VRNT VERINT SYSTEMS INC 2.5% CELG CELGENE CORP 2.4% BOH BANK OF HAWAII CORP 2.2% GIL GILDAN ACTIVEWEAR INC 2.1% LVS LAS VEGAS SANDS CORP 2.0% MLNX MELLANOX TECHNOLOGIES LTD 2.0% AAPL APPLE INC 1.9% ENS ENERSYS 1.8% ZBRA ZEBRA TECHNOLOGIES CORP 1.8% MU MICRON TECHNOLOGY INC 1.7% RYN RAYONIER INC 1.7% KLXI KLX INC 1.7% TRMB TRIMBLE INC 1.6% IRDM IRIDIUM COMMUNICATIONS INC 1.5% QCOM QUALCOMM INC 1.5% Investors should consider the investment objective and policies, risk considerations, charges and ongoing expense of an investment carefully before investing. The prospectus and summary prospectus contains this and other information relevant to an investment in the Fund. Please read the prospectus or summary prospectus carefully before you invest or send money. To obtain a prospectus, please contact your investment representative or access the Meridian Funds’ website at www.meridianfund.com. ALPS Distributors, Inc., a member FINRA is the distributor of the Meridian Mutual Funds, advised by Arrowpoint Asset Management, LLC. ALPS, Meridian and Arrowpoint are unaffiliated. Arrowpoint Partners is a trade name for Arrowpoint Asset Management, LLC., a registered investment adviser. The portfolio holdings for the Meridian Funds are published on the Funds' website on a calendar quarter basis, no earlier than 30 days after the end of the quarter. -
Unlock Bigdata Analytic Efficiency with Ceph Data Lake
Unlock Bigdata Analytic Efficiency With Ceph Data Lake Jian Zhang, Yong Fu, March, 2018 Agenda . Background & Motivations . The Workloads, Reference Architecture Evolution and Performance Optimization . Performance Comparison with Remote HDFS . Summary & Next Step 3 Challenges of scaling Hadoop* Storage BOUNDED Storage and Compute resources on Hadoop Nodes brings challenges Data Capacity Silos Costs Performance & efficiency Typical Challenges Data/Capacity Multiple Storage Silos Space, Spent, Power, Utilization Upgrade Cost Inadequate Performance Provisioning And Configuration Source: 451 Research, Voice of the Enterprise: Storage Q4 2015 *Other names and brands may be claimed as the property of others. 4 Options To Address The Challenges Compute and Large Cluster More Clusters Storage Disaggregation • Lacks isolation - • Cost of • Isolation of high- noisy neighbors duplicating priority workloads hinder SLAs datasets across • Shared big • Lacks elasticity - clusters datasets rigid cluster size • Lacks on-demand • On-demand • Can’t scale provisioning provisioning compute/storage • Can’t scale • compute/storage costs separately compute/storage costs scale costs separately separately Compute and Storage disaggregation provides Simplicity, Elasticity, Isolation 5 Unified Hadoop* File System and API for cloud storage Hadoop Compatible File System abstraction layer: Unified storage API interface Hadoop fs –ls s3a://job/ adl:// oss:// s3n:// gs:// s3:// s3a:// wasb:// 2006 2008 2014 2015 2016 6 Proposal: Apache Hadoop* with disagreed Object Storage SQL …… Hadoop Services • Virtual Machine • Container • Bare Metal HCFS Compute 1 Compute 2 Compute 3 … Compute N Object Storage Services Object Object Object Object • Co-located with gateway Storage 1 Storage 2 Storage 3 … Storage N • Dynamic DNS or load balancer • Data protection via storage replication or erasure code Disaggregated Object Storage Cluster • Storage tiering *Other names and brands may be claimed as the property of others. -
Storage for HPC and AI
Storage for HPC and AI Make breakthroughs faster with artificial intelligence powered by high performance computing systems and storage BETTER PERFORMANCE Unlock the value of data with high performance storage built for HPC. The flood of information generated by sensors, satellites, simulations, high-throughput devices and medical imaging is pushing data repositories to sizes that were once inconceivable. Data analytics, high performance computing (HPC) and artificial intelligence (AI) are technologies designed to unlock the value of all that data, driving the demand for powerful HPC systems and the storage to support them. Reliable, efficient and easy-to-adopt HPC storage is the key to enabling today’s powerful HPC systems to deliver transformative decision making, business growth and operational efficiencies in the data-driven age. Dell Technologies | Ready Solutions for HPC 2 THE INTELLIGENCE BEHIND DATA INSIGHTS Articial Machine Deep intelligence learning learning AI is a complex set of technologies underpinned by machine learning (ML) and deep learning (DL) algorithms, typically run on powerful HPC systems and storage. Together, they enable organizations to gain deeper insights from data. AI is an umbrella term that describes a machine’s ability to act autonomously and/or interact in a human-like way. ML refers to the ability of a machine to perform a programmed function with the data The capabilities of AI, ML and DL can unleash predictive and prescriptive analytics on a given to it, getting progressively better at the task over time as it analyzes more data massive scale. Like lenses, AI, ML and DL can be used in combination or alone — depending and receives feedback from users or engineers. -
And Complex-Valued Multiply-Accumulate SIMD Unit for Digital Signal Processors
An Area Efficient Real- and Complex-Valued Multiply-Accumulate SIMD Unit for Digital Signal Processors Lukas Gerlach, Guillermo Paya-Vay´ a,´ and Holger Blume Cluster of Excellence Hearing4all, Institute of Microelectronic Systems Leibniz Universitat¨ Hannover, Appelstr. 4, 30167 Hannover, Germany Email: {gerlach, guipava, blume}@ims.uni-hannover.de Abstract—This paper explores a real- and complex-valued In the signal processing field, the fast Fourier transform multiply-accumulate (MAC) functional unit for digital signal pro- (FFT) is one of the mostly used transformations, which greatly cessors. MAC units with single-instruction-multiple-data (SIMD) pushes the performance requirements. The data parallelism support are often used to increase the processing performance inherent in the FFT processing allows operating with many in modern signal processing processors. Compared to a real- independent MAC operations simultaneously. Therefore, a valued SIMD-MAC units, the proposed unit uses the same performance increment can be achieved by MAC units with multipliers to also support complex-valued SIMD-MAC and butterfly operations. The area overhead for the complex mode SIMD mechanisms, but many instructions are still needed to is small. Complex-valued operations speed up signal processing operate the real- and imaginary parts of complex numbers algorithms and make the execution more efficient in terms of separately. The use of single instructions in DSPs, executing power consumption. As a case study, a fast Fourier transform operations with complex numbers, can lead to a significant (FFT) is implemented for a VLIW-processor with a complex- performance gain in many signal processing algorithms. valued SIMD butterfly extension. The proposed functional unit is quantitatively evaluated in terms of performance, silicon area, A SIMD-MAC unit that can handle both complex and and power consumption. -
Evaluation of Active Storage Strategies for the Lustre Parallel File System
Evaluation of Active Storage Strategies for the Lustre Parallel File System Juan Piernas Jarek Nieplocha Evan J. Felix Pacific Northwest National Pacific Northwest National Pacific Northwest National Laboratory Laboratory Laboratory P.O. Box 999 P.O. Box 999 P.O. Box 999 Richland, WA 99352 Richland, WA 99352 Richland, WA 99352 [email protected] [email protected] [email protected] ABSTRACT umes of data remains a challenging problem. Despite the Active Storage provides an opportunity for reducing the improvements of storage capacities, the cost of bandwidth amount of data movement between storage and compute for moving data between the processing nodes and the stor- nodes of a parallel filesystem such as Lustre, and PVFS. age devices has not improved at the same rate as the disk ca- It allows certain types of data processing operations to be pacity. One approach to reduce the bandwidth requirements performed directly on the storage nodes of modern paral- between storage and compute devices is, when possible, to lel filesystems, near the data they manage. This is possible move computation closer to the storage devices. Similarly by exploiting the underutilized processor and memory re- to the processing-in-memory (PIM) approach for random ac- sources of storage nodes that are implemented using general cess memory [16], the active disk concept was proposed for purpose servers and operating systems. In this paper, we hard disk storage systems [1, 15, 24]. The active disk idea present a novel user-space implementation of Active Storage exploits the processing power of the embedded hard drive for Lustre, and compare it to the traditional kernel-based controller to process the data on the disk without the need implementation.