During the CXL Forum at OCP Global Summit, Intel engineer Bhushan Chitlur explains how active memory works and provides and overview of Intel's solution for disaggregated memory tiering.
Join us for an exciting and informative preview of the broadest range of next-generation systems optimized for tomorrow’s data center workloads, Powered by 4th Gen Intel® Xeon® Scalable Processors (formerly codenamed Sapphire Rapids).
Experts from Supermicro and Intel will discuss how the upcoming Supermicro X13 systems will enable new performance levels utilizing state-of-the-art technology, including DDR5, PCIe 5.0, Compute Express Link™ 1.1, and Intel® Advanced Matrix Extensions (Intel AMX).
Red Hat Storage Day Atlanta - Designing Ceph Clusters Using Intel-Based Hardw...Red_Hat_Storage
This document discusses the need for storage modernization driven by trends like mobile, social media, IoT and big data. It outlines how scale-out architectures using open source Ceph software can help meet this need more cost effectively than traditional scale-up storage. Specific optimizations for IOPS, throughput and capacity are described. Intel is presented as helping advance the industry through open source contributions and optimized platforms, software and SSD technologies. Real-world examples are given showing the wide performance range Ceph can provide.
Red Hat Storage Day New York - Intel Unlocking Big Data Infrastructure Effici...Red_Hat_Storage
This document discusses using Ceph storage with Apache Hadoop to provide a scalable and efficient storage solution for big data workloads. It outlines the challenges of scaling Hadoop storage independently from compute resources using the native Hadoop Distributed File System. The solution presented is to use the open source Ceph storage system instead of direct-attached storage. This allows Hadoop compute and storage resources to scale independently and provides a centralized storage platform for all enterprise data workloads. Performance tests showed the Ceph and Hadoop configuration providing up to a 60% improvement in I/O performance when using Intel caching software and SSDs.
From Rack scale computers to Warehouse scale computersRyousei Takano
This document discusses the transition from rack-scale computers to warehouse-scale computers through the disaggregation of technologies. It provides examples of rack-scale architectures like Open Compute Project and Intel Rack Scale Architecture. For warehouse-scale computers, it examines HP's The Machine project using application-specific cores, universal memory, and photonics fabric. It also outlines UC Berkeley's FireBox project utilizing 1 terabit/sec optical fibers, many-core systems-on-chip, and non-volatile memory modules connected via high-radix photonic switches.
One advantage of the open computing language (OpenCL) software framework is its ability to run on different architectures. Field programmable gate arrays (FPGAs) are a high-speed computing architecture used for computation acceleration. This work develops a set of eight benchmarks (memory synchronization functions, explained in this study) using an OpenCL framework to study the effect of memory access time on overall performance when targeting the general FPGA computing platform. The results indicate the best synchronization mechanism to be adopted to synthesize the proposed design on the FPGA computation architecture. The proposed research results also demonstrate the effectiveness of using a taskparallel model approach to avoid using high-cost synchronization mechanisms within proposed designs that are constructed on the general FPGA computation platform.
This document provides an overview of Gen-Z, a new interconnect architecture proposed to address challenges with increasing data growth, flat memory capacity, and the need for real-time data insights. Gen-Z is designed to provide high bandwidth and low latency memory semantic communications across systems. It breaks the traditional processor-memory interlock by introducing a split controller model. This allows for more flexible and composable solutions that can leverage different memory technologies. The Gen-Z Consortium is developing open standards for the architecture with the goal of enabling innovation through an open and non-proprietary approach.
Join us for an exciting and informative preview of the broadest range of next-generation systems optimized for tomorrow’s data center workloads, Powered by 4th Gen Intel® Xeon® Scalable Processors (formerly codenamed Sapphire Rapids).
Experts from Supermicro and Intel will discuss how the upcoming Supermicro X13 systems will enable new performance levels utilizing state-of-the-art technology, including DDR5, PCIe 5.0, Compute Express Link™ 1.1, and Intel® Advanced Matrix Extensions (Intel AMX).
Red Hat Storage Day Atlanta - Designing Ceph Clusters Using Intel-Based Hardw...Red_Hat_Storage
This document discusses the need for storage modernization driven by trends like mobile, social media, IoT and big data. It outlines how scale-out architectures using open source Ceph software can help meet this need more cost effectively than traditional scale-up storage. Specific optimizations for IOPS, throughput and capacity are described. Intel is presented as helping advance the industry through open source contributions and optimized platforms, software and SSD technologies. Real-world examples are given showing the wide performance range Ceph can provide.
Red Hat Storage Day New York - Intel Unlocking Big Data Infrastructure Effici...Red_Hat_Storage
This document discusses using Ceph storage with Apache Hadoop to provide a scalable and efficient storage solution for big data workloads. It outlines the challenges of scaling Hadoop storage independently from compute resources using the native Hadoop Distributed File System. The solution presented is to use the open source Ceph storage system instead of direct-attached storage. This allows Hadoop compute and storage resources to scale independently and provides a centralized storage platform for all enterprise data workloads. Performance tests showed the Ceph and Hadoop configuration providing up to a 60% improvement in I/O performance when using Intel caching software and SSDs.
From Rack scale computers to Warehouse scale computersRyousei Takano
This document discusses the transition from rack-scale computers to warehouse-scale computers through the disaggregation of technologies. It provides examples of rack-scale architectures like Open Compute Project and Intel Rack Scale Architecture. For warehouse-scale computers, it examines HP's The Machine project using application-specific cores, universal memory, and photonics fabric. It also outlines UC Berkeley's FireBox project utilizing 1 terabit/sec optical fibers, many-core systems-on-chip, and non-volatile memory modules connected via high-radix photonic switches.
One advantage of the open computing language (OpenCL) software framework is its ability to run on different architectures. Field programmable gate arrays (FPGAs) are a high-speed computing architecture used for computation acceleration. This work develops a set of eight benchmarks (memory synchronization functions, explained in this study) using an OpenCL framework to study the effect of memory access time on overall performance when targeting the general FPGA computing platform. The results indicate the best synchronization mechanism to be adopted to synthesize the proposed design on the FPGA computation architecture. The proposed research results also demonstrate the effectiveness of using a taskparallel model approach to avoid using high-cost synchronization mechanisms within proposed designs that are constructed on the general FPGA computation platform.
This document provides an overview of Gen-Z, a new interconnect architecture proposed to address challenges with increasing data growth, flat memory capacity, and the need for real-time data insights. Gen-Z is designed to provide high bandwidth and low latency memory semantic communications across systems. It breaks the traditional processor-memory interlock by introducing a split controller model. This allows for more flexible and composable solutions that can leverage different memory technologies. The Gen-Z Consortium is developing open standards for the architecture with the goal of enabling innovation through an open and non-proprietary approach.
Red hat Storage Day LA - Designing Ceph Clusters Using Intel-Based HardwareRed_Hat_Storage
This document discusses how data growth driven by mobile, social media, IoT, and big data/cloud is requiring a fundamental shift in storage cost structures from scale-up to scale-out architectures. It provides an overview of key storage technologies and workloads driving public cloud storage, and how Ceph can help deliver on the promise of the cloud by providing next generation storage architectures with flash to enable new capabilities in small footprints. It also illustrates the wide performance range Ceph can provide for different workloads and hardware configurations.
Accelerate Your Apache Spark with Intel Optane DC Persistent MemoryDatabricks
The capacity of data grows rapidly in big data area, more and more memory are consumed either in the computation or holding the intermediate data for analytic jobs. For those memory intensive workloads, end-point users have to scale out the computation cluster or extend memory with storage like HDD or SSD to meet the requirement of computing tasks. For scaling out the cluster, the extra cost from cluster management, operation and maintenance will increase the total cost if the extra CPU resources are not fully utilized. To address the shortcoming above, Intel Optane DC persistent memory (Optane DCPM) breaks the traditional memory/storage hierarchy and scale up the computing server with higher capacity persistent memory. Also it brings higher bandwidth & lower latency than storage like SSD or HDD. And Apache Spark is widely used in the analytics like SQL and Machine Learning on the cloud environment. For cloud environment, low performance of remote data access is typical a stop gap for users especially for some I/O intensive queries. For the ML workload, it's an iterative model which I/O bandwidth is the key to the end-2-end performance. In this talk, we will introduce how to accelerate Spark SQL with OAP (https://github.com/Intel-bigdata/OAP) to accelerate SQL performance on Cloud to archive 8X performance gain and RDD cache to improve K-means performance with 2.5X performance gain leveraging Intel Optane DCPM. Also we will have a deep dive how Optane DCPM for these performance gains.
Speakers: Cheng Xu, Piotr Balcer
Implementation of RISC-Based Architecture for Low power applicationsIOSR Journals
This document describes the design and implementation of a 32-bit RISC processor intended for low power applications. The processor was developed using VHDL and implemented on a Xilinx Spartan-3E FPGA. It has a simple instruction set, program and data memories, general purpose registers, and an ALU. The processor follows a multi-cycle execution model. Simulation results show the processor executing instructions correctly in a single clock cycle, as intended for a RISC design. Power analysis shows the proposed RISC architecture consumes 132mW of power, half of previous designs, demonstrating its efficiency for low power applications.
In this paper, proposed a novel implementation of a Soft-Core system using
micro-blaze processor with virtex-5 FPGA. Till now Hard-Core processors are used in
FPGA processor cores. Hard cores are a fixed gate-level IP functions within the FPGA
fabrics. Now the proposed processor is Soft-Core Processor, this is a microprocessor fully
described in software, usually in an HDL. This can be implemented by using EDK tool. In
this paper, developed a system which is having a micro-blaze processor is the combination
of both hardware & Software. By using this system, user can control and communicate all
the peripherals which are in the supported board by using Xilinx platform to develop an
embedded system. Implementing of Soft-Core process system with different peripherals like
UART interface, SPA flash interface, SRAM interface has to be designed using Xilinx
Embedded Development Kit (EDK) tools.
Building an open memory-centric computing architecture using intel optaneUniFabric
OOW 2017 presentation showcasing Fabric Attached Memory with 2 node RAC system based on two standard x86 servers running 200 GB/s with a per licensed cpu core data rate of 25 GB/s.
XPDS16: High-Performance Virtualization for HPC Cloud on Xen - Jun Nakajima &...The Linux Foundation
We have been working to get Xen up and running on self-boot Intel® Xeon Phi processors to build HPC clouds. We see several challenges because of the unique (but not unusual for HPC) hardware technologies and performance requirements. For example, such hardware technologies include 1) >256 CPUs, 2) MCDRAM (high-bandwidth memory), 3) integrated fabric (i.e. Intel® Omni-Path). Unlike the “coprocessor“ model, supporting self-boot with >256 CPUs has various implications to Xen, including scheduling and scalability. We need to allow user applications to use MCDRAM directly to perform optimally. Also, we need to enable the integrated HPC fabric for the VM to use by direct I/O assignment.
In addition, we have only a single VM on each node to meet the high-performance requirements of HPC clouds. This (i.e. non-shared) model allowed us to optimize Xen more. In this talk, we share our design and lessons, and discuss the options we considered to achieve high-performance virtualization for HPC.
The document discusses several types of processors including Pentium 4, dual-core, and quad-core processors, explaining their features and advantages. Pentium 4 used the NetBurst architecture but faced challenges scaling to higher speeds. Dual-core and quad-core processors place multiple processor cores on a single chip to improve performance through parallel processing while reducing power needs.
Pedal to the Metal: Accelerating Spark with Silicon InnovationJen Aman
Pedal to the Metal: Accelerating Spark* with Silicon Innovation discusses how Intel is optimizing the Spark analytics framework for better performance. Hardware optimizations like Intel Xeon CPUs, Intel Optane DC persistent memory, and Intel Omni-Path Architecture can accelerate Spark workloads by up to 7x. Intel is also contributing open source code to Spark projects and optimizing machine learning libraries like MLLib to further boost Spark performance using Intel Math Kernel Library. The document advocates that these hardware and software optimizations can help Spark be more easily deployed for analytics, machine learning, and IoT applications.
Ceph Day Beijing - Storage Modernization with Intel and CephDanielle Womboldt
The document discusses trends in data growth and storage technologies that are driving the need for storage modernization. It outlines Intel's role in advancing the storage industry through open source technologies and standards. A significant portion of the document focuses on Intel's work optimizing Ceph for Intel platforms, including profiling and benchmarking Ceph performance on Intel SSDs, 3D XPoint, and Optane drives.
Ceph Day Beijing - Storage Modernization with Intel & Ceph Ceph Community
The document discusses trends in data growth and storage technologies that are driving the need for storage modernization. It outlines Intel's role in advancing the storage industry through open source technologies and standards. Specifically, it focuses on Intel's work optimizing Ceph for Intel platforms, including performance profiling, enabling Intel optimized solutions, and end customer proofs-of-concept using Ceph with Intel SSDs, Optane, and platforms.
Deep Learning Training at Scale: Spring Crest Deep Learning Acceleratorinside-BigData.com
Today at Hot Chips 2019, Intel revealed new details of upcoming high-performance AI accelerators: Intel Nervana neural network processors, with the NNP-T for training and the NNP-I for inference. Intel engineers also presented technical details on hybrid chip packaging technology, Intel Optane DC persistent memory and chiplet technology for optical I/O.
"To get to a future state of ‘AI everywhere,’ we’ll need to address the crush of data being generated and ensure enterprises are empowered to make efficient use of their data, processing it where it’s collected when it makes sense and making smarter use of their upstream resources," said Naveen Rao, Intel vice president and GM, Artificial Intelligence Products Group. "Data centers and the cloud need to have access to performant and scalable general purpose computing and specialized acceleration for complex AI applications. In this future vision of AI everywhere, a holistic approach is needed—from hardware to software to applications.”
Learn more: https://www.intel.ai/accelerating-for-ai/?elq_cid=1192980
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Reliable Hydra SSD Architecture for General Purpose ControllersIJMER
The Solid State Disks (SSDs) had almost replaced the traditional Hard Disk Drives (HDDs) in modern computing systems. SSDs possess advanced features such as low power consumption, faster random access and greater shock resistance. In general, NAND Flash memories are used for bulk storage applications. This project focus on an advanced SSD architecture, called Reliable Hydra to enhance the SSD performance. Hydra SSDs overcomes the discrepancy between slow flash memory bus and fast host interfaces like SATA, SCSI, USB etc. It uses multiple high level memory controllers to execute flash memory operation without the intervention of FTL (Flash Translation Layer). It accelerates the processing of host write requests by aggressive write buffering. Memories are subjected to bit flipping errors, so this project also considers the incorporation of matrix code for increasing the reliability and hence yield of the system. The highly sophisticated controllers for real time systems in the industrial, robotics, medical and scientific applications require high performance and reliable memories. The aim of this project is to design Reliable Hydra SSD architecture for controller applications to enhance the performance. The design architecture is to be coded in VHDL using Xilinx ISE tools.
Mark Zuckerberg has funded a project called Aquila, which is an unmanned solar-powered airplane that can stay in the air for months at a time. Its purpose is to beam internet connectivity from the sky. It has the wingspan of a Boeing 737 but weighs less than a car. Embedded blocks such as memory, processors and DSP units are commonly used in FPGAs to improve performance, power efficiency and resource utilization for the target application. However, unused blocks result in silicon waste.
DAOS - Scale-Out Software-Defined Storage for HPC/Big Data/AI Convergenceinside-BigData.com
In this deck, Johann Lombardi from Intel presents: DAOS - Scale-Out Software-Defined Storage for HPC/Big Data/AI Convergence.
"Intel has been building an entirely open source software ecosystem for data-centric computing, fully optimized for Intel® architecture and non-volatile memory (NVM) technologies, including Intel Optane DC persistent memory and Intel Optane DC SSDs. Distributed Asynchronous Object Storage (DAOS) is the foundation of the Intel exascale storage stack. DAOS is an open source software-defined scale-out object store that provides high bandwidth, low latency, and high I/O operations per second (IOPS) storage containers to HPC applications. It enables next-generation data-centric workflows that combine simulation, data analytics, and AI."
Unlike traditional storage stacks that were primarily designed for rotating media, DAOS is architected from the ground up to make use of new NVM technologies, and it is extremely lightweight because it operates end-to-end in user space with full operating system bypass. DAOS offers a shift away from an I/O model designed for block-based, high-latency storage to one that inherently supports fine- grained data access and unlocks the performance of next- generation storage technologies.
Watch the video: https://youtu.be/wnGBW31yhLM
Learn more: https://www.intel.com/content/www/us/en/high-performance-computing/daos-high-performance-storage-brief.html
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Complete data analysis faster with Google Cloud C3 high CPU instances enabled...Principled Technologies
Compared to N2 high CPU instances with previous-gen processors, these instances sped up query completion times
Conclusion
Data collected, but not analyzed, offers only potential, not insights, to an organization. Sorting and analyzing that data can turn rows and tables into meaningful insights about what customers want or which business initiatives should proceed. The faster an organization gets insights from data, the quicker its leaders can make decisions. In our tests, where both instance types used Google Cloud Hyperdisk Extreme storage and were configured similarly, Google Cloud C3 high CPU instances with 4th Gen Intel Xeon Scalable processors delivered 1.88x the query completion speeds of N2 high CPU instances with previous-gen processors. Selecting these newer C3 high CPU instances with Hyperdisk can help ensure that data analysis completes within targeted windows and give decision makers the insights they need to quickly adjust to the changing tides of business and realize success.
Reimagining HPC Compute and Storage Architecture with Intel Optane Technologyinside-BigData.com
In this deck from the DDN User Group at SC19, Andrey Kudryavtsev from Intel presents: Reimagining HPC Compute and Storage Architecture with Intel Optane Technology.
"In the face of unrelenting data growth, rising numbers of high-performance computing (HPC) workloads are memory bound. Caught between the high cost and limited capacity of DRAM and the lower performance of 3D NAND SSDs, HPC users increasingly find that despite workarounds, they’re unable to keep pace with skyrocketing data volumes and increasingly complex challenges. Intel Optane technology is designed to address these challenges. Available as Intel Optane DC persistent memory and Intel Optane DC Solid State Drives (Intel Optane SSDs), this technology closes the capacity, cost, and performance gaps between DRAM and 3D NAND SSDs, providing opportunities to advance data-intensive workloads while increasing uptime and flexibility in the HPC data center."
Watch the video: https://wp.me/p3RLHQ-ljl
Learn more: https://www.intel.com/content/dam/www/public/us/en/documents/technology-briefs/what-is-optane-technology-brief.pdf
and
https://www.ddn.com/company/events/user-group-sc/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This document summarizes research comparing the performance of single-core and multi-core processors when performing matrix multiplication. It finds that a multi-core processor, the Cell Broad Engine with 8 cores, was about 8 times faster than a single Power Processing Element core. However, performance began to decline for matrices larger than 2048x2048 due to data being swapped to disk. It also compares two multi-core programming models, Sequoia and Cellgen, finding Sequoia more stable but Cellgen having higher performance in other research.
Accelerating Virtual Machine Access with the Storage Performance Development ...Michelle Holley
Abstract: Although new non-volatile media inherently offers very low latency, remote access
using protocols such as NVMe-oF and presenting the data to VMs via virtualized interfaces such as virtio
adds considerable software overhead. One way to reduce the overhead is to use the Storage
Performance Development Kit (SPDK), an open-source software project that provides building blocks for
scalable and efficient storage applications with breakthrough performance. Comparing the software
paths for virtualizing block storage I/O illustrates the advantages of the SPDK-based approach. Empirical
data shows that using SPDK can improve CPU efficiency by up to 10 x and reduce latency up to 50% over
existing methods. Future enhancements for SPDK will make its advantages even greater.
Speaker Bio: Anu Rao is Product line manager for storage software in Data center Group. She helps
customer ease into and adopt open source Storage software like Storage Performance Development Kit
(SPDK) and Intelligent Software Acceleration-Library (ISA-L).
Q1 Memory Fabric Forum: ZeroPoint. Remove the waste. Release the power.Memory Fabric Forum
Nilesh Shah provide an overview of the ZeroPoint portable, hardware IP portfolio for lossless memory compression and compaction. The IP boosts memory capacity 2-4x, bandwidth and performance/watt by 50%, and is 1,000x faster than competitors.
More Related Content
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Red hat Storage Day LA - Designing Ceph Clusters Using Intel-Based HardwareRed_Hat_Storage
This document discusses how data growth driven by mobile, social media, IoT, and big data/cloud is requiring a fundamental shift in storage cost structures from scale-up to scale-out architectures. It provides an overview of key storage technologies and workloads driving public cloud storage, and how Ceph can help deliver on the promise of the cloud by providing next generation storage architectures with flash to enable new capabilities in small footprints. It also illustrates the wide performance range Ceph can provide for different workloads and hardware configurations.
Accelerate Your Apache Spark with Intel Optane DC Persistent MemoryDatabricks
The capacity of data grows rapidly in big data area, more and more memory are consumed either in the computation or holding the intermediate data for analytic jobs. For those memory intensive workloads, end-point users have to scale out the computation cluster or extend memory with storage like HDD or SSD to meet the requirement of computing tasks. For scaling out the cluster, the extra cost from cluster management, operation and maintenance will increase the total cost if the extra CPU resources are not fully utilized. To address the shortcoming above, Intel Optane DC persistent memory (Optane DCPM) breaks the traditional memory/storage hierarchy and scale up the computing server with higher capacity persistent memory. Also it brings higher bandwidth & lower latency than storage like SSD or HDD. And Apache Spark is widely used in the analytics like SQL and Machine Learning on the cloud environment. For cloud environment, low performance of remote data access is typical a stop gap for users especially for some I/O intensive queries. For the ML workload, it's an iterative model which I/O bandwidth is the key to the end-2-end performance. In this talk, we will introduce how to accelerate Spark SQL with OAP (https://github.com/Intel-bigdata/OAP) to accelerate SQL performance on Cloud to archive 8X performance gain and RDD cache to improve K-means performance with 2.5X performance gain leveraging Intel Optane DCPM. Also we will have a deep dive how Optane DCPM for these performance gains.
Speakers: Cheng Xu, Piotr Balcer
Implementation of RISC-Based Architecture for Low power applicationsIOSR Journals
This document describes the design and implementation of a 32-bit RISC processor intended for low power applications. The processor was developed using VHDL and implemented on a Xilinx Spartan-3E FPGA. It has a simple instruction set, program and data memories, general purpose registers, and an ALU. The processor follows a multi-cycle execution model. Simulation results show the processor executing instructions correctly in a single clock cycle, as intended for a RISC design. Power analysis shows the proposed RISC architecture consumes 132mW of power, half of previous designs, demonstrating its efficiency for low power applications.
In this paper, proposed a novel implementation of a Soft-Core system using
micro-blaze processor with virtex-5 FPGA. Till now Hard-Core processors are used in
FPGA processor cores. Hard cores are a fixed gate-level IP functions within the FPGA
fabrics. Now the proposed processor is Soft-Core Processor, this is a microprocessor fully
described in software, usually in an HDL. This can be implemented by using EDK tool. In
this paper, developed a system which is having a micro-blaze processor is the combination
of both hardware & Software. By using this system, user can control and communicate all
the peripherals which are in the supported board by using Xilinx platform to develop an
embedded system. Implementing of Soft-Core process system with different peripherals like
UART interface, SPA flash interface, SRAM interface has to be designed using Xilinx
Embedded Development Kit (EDK) tools.
Building an open memory-centric computing architecture using intel optaneUniFabric
OOW 2017 presentation showcasing Fabric Attached Memory with 2 node RAC system based on two standard x86 servers running 200 GB/s with a per licensed cpu core data rate of 25 GB/s.
XPDS16: High-Performance Virtualization for HPC Cloud on Xen - Jun Nakajima &...The Linux Foundation
We have been working to get Xen up and running on self-boot Intel® Xeon Phi processors to build HPC clouds. We see several challenges because of the unique (but not unusual for HPC) hardware technologies and performance requirements. For example, such hardware technologies include 1) >256 CPUs, 2) MCDRAM (high-bandwidth memory), 3) integrated fabric (i.e. Intel® Omni-Path). Unlike the “coprocessor“ model, supporting self-boot with >256 CPUs has various implications to Xen, including scheduling and scalability. We need to allow user applications to use MCDRAM directly to perform optimally. Also, we need to enable the integrated HPC fabric for the VM to use by direct I/O assignment.
In addition, we have only a single VM on each node to meet the high-performance requirements of HPC clouds. This (i.e. non-shared) model allowed us to optimize Xen more. In this talk, we share our design and lessons, and discuss the options we considered to achieve high-performance virtualization for HPC.
The document discusses several types of processors including Pentium 4, dual-core, and quad-core processors, explaining their features and advantages. Pentium 4 used the NetBurst architecture but faced challenges scaling to higher speeds. Dual-core and quad-core processors place multiple processor cores on a single chip to improve performance through parallel processing while reducing power needs.
Pedal to the Metal: Accelerating Spark with Silicon InnovationJen Aman
Pedal to the Metal: Accelerating Spark* with Silicon Innovation discusses how Intel is optimizing the Spark analytics framework for better performance. Hardware optimizations like Intel Xeon CPUs, Intel Optane DC persistent memory, and Intel Omni-Path Architecture can accelerate Spark workloads by up to 7x. Intel is also contributing open source code to Spark projects and optimizing machine learning libraries like MLLib to further boost Spark performance using Intel Math Kernel Library. The document advocates that these hardware and software optimizations can help Spark be more easily deployed for analytics, machine learning, and IoT applications.
Ceph Day Beijing - Storage Modernization with Intel and CephDanielle Womboldt
The document discusses trends in data growth and storage technologies that are driving the need for storage modernization. It outlines Intel's role in advancing the storage industry through open source technologies and standards. A significant portion of the document focuses on Intel's work optimizing Ceph for Intel platforms, including profiling and benchmarking Ceph performance on Intel SSDs, 3D XPoint, and Optane drives.
Ceph Day Beijing - Storage Modernization with Intel & Ceph Ceph Community
The document discusses trends in data growth and storage technologies that are driving the need for storage modernization. It outlines Intel's role in advancing the storage industry through open source technologies and standards. Specifically, it focuses on Intel's work optimizing Ceph for Intel platforms, including performance profiling, enabling Intel optimized solutions, and end customer proofs-of-concept using Ceph with Intel SSDs, Optane, and platforms.
Deep Learning Training at Scale: Spring Crest Deep Learning Acceleratorinside-BigData.com
Today at Hot Chips 2019, Intel revealed new details of upcoming high-performance AI accelerators: Intel Nervana neural network processors, with the NNP-T for training and the NNP-I for inference. Intel engineers also presented technical details on hybrid chip packaging technology, Intel Optane DC persistent memory and chiplet technology for optical I/O.
"To get to a future state of ‘AI everywhere,’ we’ll need to address the crush of data being generated and ensure enterprises are empowered to make efficient use of their data, processing it where it’s collected when it makes sense and making smarter use of their upstream resources," said Naveen Rao, Intel vice president and GM, Artificial Intelligence Products Group. "Data centers and the cloud need to have access to performant and scalable general purpose computing and specialized acceleration for complex AI applications. In this future vision of AI everywhere, a holistic approach is needed—from hardware to software to applications.”
Learn more: https://www.intel.ai/accelerating-for-ai/?elq_cid=1192980
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Reliable Hydra SSD Architecture for General Purpose ControllersIJMER
The Solid State Disks (SSDs) had almost replaced the traditional Hard Disk Drives (HDDs) in modern computing systems. SSDs possess advanced features such as low power consumption, faster random access and greater shock resistance. In general, NAND Flash memories are used for bulk storage applications. This project focus on an advanced SSD architecture, called Reliable Hydra to enhance the SSD performance. Hydra SSDs overcomes the discrepancy between slow flash memory bus and fast host interfaces like SATA, SCSI, USB etc. It uses multiple high level memory controllers to execute flash memory operation without the intervention of FTL (Flash Translation Layer). It accelerates the processing of host write requests by aggressive write buffering. Memories are subjected to bit flipping errors, so this project also considers the incorporation of matrix code for increasing the reliability and hence yield of the system. The highly sophisticated controllers for real time systems in the industrial, robotics, medical and scientific applications require high performance and reliable memories. The aim of this project is to design Reliable Hydra SSD architecture for controller applications to enhance the performance. The design architecture is to be coded in VHDL using Xilinx ISE tools.
Mark Zuckerberg has funded a project called Aquila, which is an unmanned solar-powered airplane that can stay in the air for months at a time. Its purpose is to beam internet connectivity from the sky. It has the wingspan of a Boeing 737 but weighs less than a car. Embedded blocks such as memory, processors and DSP units are commonly used in FPGAs to improve performance, power efficiency and resource utilization for the target application. However, unused blocks result in silicon waste.
DAOS - Scale-Out Software-Defined Storage for HPC/Big Data/AI Convergenceinside-BigData.com
In this deck, Johann Lombardi from Intel presents: DAOS - Scale-Out Software-Defined Storage for HPC/Big Data/AI Convergence.
"Intel has been building an entirely open source software ecosystem for data-centric computing, fully optimized for Intel® architecture and non-volatile memory (NVM) technologies, including Intel Optane DC persistent memory and Intel Optane DC SSDs. Distributed Asynchronous Object Storage (DAOS) is the foundation of the Intel exascale storage stack. DAOS is an open source software-defined scale-out object store that provides high bandwidth, low latency, and high I/O operations per second (IOPS) storage containers to HPC applications. It enables next-generation data-centric workflows that combine simulation, data analytics, and AI."
Unlike traditional storage stacks that were primarily designed for rotating media, DAOS is architected from the ground up to make use of new NVM technologies, and it is extremely lightweight because it operates end-to-end in user space with full operating system bypass. DAOS offers a shift away from an I/O model designed for block-based, high-latency storage to one that inherently supports fine- grained data access and unlocks the performance of next- generation storage technologies.
Watch the video: https://youtu.be/wnGBW31yhLM
Learn more: https://www.intel.com/content/www/us/en/high-performance-computing/daos-high-performance-storage-brief.html
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Complete data analysis faster with Google Cloud C3 high CPU instances enabled...Principled Technologies
Compared to N2 high CPU instances with previous-gen processors, these instances sped up query completion times
Conclusion
Data collected, but not analyzed, offers only potential, not insights, to an organization. Sorting and analyzing that data can turn rows and tables into meaningful insights about what customers want or which business initiatives should proceed. The faster an organization gets insights from data, the quicker its leaders can make decisions. In our tests, where both instance types used Google Cloud Hyperdisk Extreme storage and were configured similarly, Google Cloud C3 high CPU instances with 4th Gen Intel Xeon Scalable processors delivered 1.88x the query completion speeds of N2 high CPU instances with previous-gen processors. Selecting these newer C3 high CPU instances with Hyperdisk can help ensure that data analysis completes within targeted windows and give decision makers the insights they need to quickly adjust to the changing tides of business and realize success.
Reimagining HPC Compute and Storage Architecture with Intel Optane Technologyinside-BigData.com
In this deck from the DDN User Group at SC19, Andrey Kudryavtsev from Intel presents: Reimagining HPC Compute and Storage Architecture with Intel Optane Technology.
"In the face of unrelenting data growth, rising numbers of high-performance computing (HPC) workloads are memory bound. Caught between the high cost and limited capacity of DRAM and the lower performance of 3D NAND SSDs, HPC users increasingly find that despite workarounds, they’re unable to keep pace with skyrocketing data volumes and increasingly complex challenges. Intel Optane technology is designed to address these challenges. Available as Intel Optane DC persistent memory and Intel Optane DC Solid State Drives (Intel Optane SSDs), this technology closes the capacity, cost, and performance gaps between DRAM and 3D NAND SSDs, providing opportunities to advance data-intensive workloads while increasing uptime and flexibility in the HPC data center."
Watch the video: https://wp.me/p3RLHQ-ljl
Learn more: https://www.intel.com/content/dam/www/public/us/en/documents/technology-briefs/what-is-optane-technology-brief.pdf
and
https://www.ddn.com/company/events/user-group-sc/
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This document summarizes research comparing the performance of single-core and multi-core processors when performing matrix multiplication. It finds that a multi-core processor, the Cell Broad Engine with 8 cores, was about 8 times faster than a single Power Processing Element core. However, performance began to decline for matrices larger than 2048x2048 due to data being swapped to disk. It also compares two multi-core programming models, Sequoia and Cellgen, finding Sequoia more stable but Cellgen having higher performance in other research.
Accelerating Virtual Machine Access with the Storage Performance Development ...Michelle Holley
Abstract: Although new non-volatile media inherently offers very low latency, remote access
using protocols such as NVMe-oF and presenting the data to VMs via virtualized interfaces such as virtio
adds considerable software overhead. One way to reduce the overhead is to use the Storage
Performance Development Kit (SPDK), an open-source software project that provides building blocks for
scalable and efficient storage applications with breakthrough performance. Comparing the software
paths for virtualizing block storage I/O illustrates the advantages of the SPDK-based approach. Empirical
data shows that using SPDK can improve CPU efficiency by up to 10 x and reduce latency up to 50% over
existing methods. Future enhancements for SPDK will make its advantages even greater.
Speaker Bio: Anu Rao is Product line manager for storage software in Data center Group. She helps
customer ease into and adopt open source Storage software like Storage Performance Development Kit
(SPDK) and Intelligent Software Acceleration-Library (ISA-L).
Similar to Intel: CXL Enabled Heterogeneous Active Memory Tiering (20)
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Q1 Memory Fabric Forum: Using CXL with AI Applications - Steve Scargall.pptxMemory Fabric Forum
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Torry Steed, Sr. Product Marketing Manager at SMART Modular, provides an overview of CXL PCIe Add-in Cards (AICs) and memory modules that can be used to expand capacity in servers or in external memory pooling systems.
Torry Steed, Sr. Staff Product Manager at SMART Modular, covers the changing shape of memory leading to new categories of CXL form factors. He dives deeper to address EDSFF and AIC variations, mechanical sizes, installation locations, capacity considerations, and power ratings.
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MemVerge CEO Charles Fan describes why memory-hungry generative AI is a driver for CXL technology, the new computing model for AI, and MemVerge software for CXL and AI.
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Q1 Memory Fabric Forum: Compute Express Link (CXL) 3.1 UpdateMemory Fabric Forum
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Q1 Memory Fabric Forum: Advantages of Optical CXL for Disaggregated Compute ...Memory Fabric Forum
Ron Swartzentruber, Director of Engineering at Lightelligence, explains why optical connectivity is needed for CXL fabrics, and provides an overview of the Photowave line of port expander PCIe cards and active optical cables.
Q1 Memory Fabric Forum: Intel Enabling Compute Express Link (CXL)Memory Fabric Forum
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Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
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Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
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Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Dr. Sean Tan, Head of Data Science, Changi Airport Group
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How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
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Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
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Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
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Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
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Mental Health: Maintaining balance and not feeling pressured by user demands.
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Explore more about Albumentations and join the community at:
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In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
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Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
2. Exploring Heterogenous Memory Tiers
to Improve Performance and Lower
TCO
CXL Enabled Heterogeneous Active Memory
Tiering
3. Application’s use less memory than what is allocated to them
Background : The Datacenter Memory
Conundrum
Low Perf,
Low Cost,
High Capacity
Improve TCO by effective use of heterogenous Memory Tiers
Hot Pages
Cold
Pages
Warm
Pages
Best Perf,
High Cost,
Low Capacity
Credit - https://www.pdl.cmu.edu/ftp/NVM/tmo_asplos22.pdf
Cost of memory, compressed memory, and SSDs as a percentage of
compute infrastructure across hardware generations
8. • The time is right to explore Heterogenous memory tiering
• FPGAs are very well suited to support the varied requirements of active tiering
• Portfolio of technologies required for active tiering are available to build scalable
solutions
• Check out the Demo “Improve System Performance by Offloading Memory-Intensive
Kernel Features to CXL Type-2 Device” in Experience Center
Where to find additional information
• Intel Agilex® 7 FPGA I-Series FPGA Development Kit
https://www.intel.com/content/www/us/en/products/details/fpga/development-kits/agilex/agi027.html
• Intel CXL IP
https://www.intel.com/content/www/us/en/products/details/fpga/intellectual-property/interface-
protocols/cxl-ip.html
• Intel’s CXL products and solutions, contact, Pekon Gupta (pekon.gupta@intel.com)
Call to Action
11. [TRACK NAME]
• Sustainability
• Special Focus: Deployments
• Special Focus: Optical Connectivity
for AI Clusters
• Special Focus: Quantum
• Special Focus: Sustainable
Computational Infrastructure for AI
• TAP (Time Appliances Project)
• CE (Cooling Environments)
• Data Center Facilities (DCF) and Rack &
Power
• Edge
• Hardware Management with DC-SCM
• Networking
• Open System Firmware (OSF)
• Security
• Server
• Server: Composable Memory Systems
• Server: Open Chiplet Economy
• Storage
12. SERVER
NETWORKING STORAGE
DC FACILITIES HW MGMT
SECURITY
OPEN SYS FW
TIME
APPLIANCES
COOLING ENV
Please use the appropriate icon representing the Project Group.
SUSTAINABILI
TY
RACK &
POWER
TELCO
AI OPTICS
OPEN
CHIPLET
CMS
DEPLOYMENT
S
13. Click here to review OCP Trademark
Guidelines.
Please use the appropriate icon representing the Regional Project Group.