Knowing what's inside and how it works will help you design, develop, and implement applications better, faster, cheaper, more efficient, and easier to use because you will be able to make informed decisions instead of guestimating and assuming.
Convolutional Neural Networks (CNNs) have achieved state-of-the-art accuracy on a variety of computer vision tasks. However, this accuracy comes at a high computational cost which is typically not met by general purpose processor.
In the recent years, different ad-hoc accelerators have been proposed to tackle this issue, and in particular, FPGAs seems to be a promising candidate for acceleration. Unfortunately, while there are many different deep learning libraries to train convolutional neural networks, creating an FPGA-based hardware accelerator is still a manual and complex task that requires time and vertical knowledge.
CONDOR is a framework to automatically derive an FPGA-based hardware accelerator starting from a high-level description of a pre-trained CNN. The goal is to make FPGA more accessible to deep learning users, offering a quick and automated way to deploy CNNs on reconfigurable hardware.
In the first part of this talk, we provide an overview of the framework and its possibilities regarding integrations with existing libraries and available deployment options, while in the second part, we dive into the architectural details of the underlying accelerator.
Convolutional Neural Networks (CNNs) have achieved state-of-the-art accuracy on a variety of computer vision tasks. However, this accuracy comes at a high computational cost which is typically not met by general purpose processor.
In the recent years, different ad-hoc accelerators have been proposed to tackle this issue, and in particular, FPGAs seems to be a promising candidate for acceleration. Unfortunately, while there are many different deep learning libraries to train convolutional neural networks, creating an FPGA-based hardware accelerator is still a manual and complex task that requires time and vertical knowledge.
CONDOR is a framework to automatically derive an FPGA-based hardware accelerator starting from a high-level description of a pre-trained CNN. The goal is to make FPGA more accessible to deep learning users, offering a quick and automated way to deploy CNNs on reconfigurable hardware.
In the first part of this talk, we provide an overview of the framework and its possibilities regarding integrations with existing libraries and available deployment options, while in the second part, we dive into the architectural details of the underlying accelerator.
AMD Bridges the X86 and ARM Ecosystems for the Data Center AMD
Presentation by Lisa Su, senior vice president and general manager, Global Business Units, AMD regarding AMD’s announcement that it will design and build 64-bit ARM technology-based processors.
This presentation was part of JNTU A 2 days workshops where IBM expert Satish presented about Open POWER ISA , Open Cores and the future architectures ..
This lesson on System-on-Chip was given for the course "Advanced Platform Architectures and Mapping Methods for Embedded Applications" at the KU Leuven and is based on chapter 8 of 'A Practical Introduction to Hardware Software Codesign (Schaumont P.)'
1. CISC VS. RISC.
2. Agenda.
3. CPU Architecture.
4. Instruction Set Architecture (ISA). Group of instructions to execute a program. Instructions are in the form of: Opcode + Operand. An agreement between hardware and human for making interaction. Example : ADD R1, R2, R3
Can be represented as :
00101111100001111001010101010101
10111010100011110101001011011010
Two major schools of ISA: CISC & RISC.
5. CISC Philosophy (Complex Instruction Set Computing). The primary goal is to complete a task in as few lines as possible. Used on PCs and laptops that need to process heavy graphics and computations. Each instruction consist of one step.
(ex: MULT 2:3, 5:2, load the two values into registers, multiplies the operands, and then stores the product in appropriate register).
6. CISC Pros & Cons. Instruction size is different from one operation to another. Operation size is smaller but no of cycles are more. Needs better hardware and powerful processing. Performance is slow due to the amount of clock time taken by different instructions.
7. RISC Philosophy (Reduced Instruction Set Computing). Use only simple instructions that can be executed within one clock cycle. Keep all instructions of same size. Allow only load/store instruction to access the memory.
(ex: MULT command divided into three separate commands:LOAD, PROD, and STORE).
8. RISC Pros & Cons. Allow free use of microprocessors space because of its simplicity. Needs large memory caches on the chip itself so require very fast memory. Give support for high level languages (like C, C++, Java). Performance depends on the programmer or compiler.
9. CPU Performance Equation. The following equation is commonly used for expressing a computer's performance ability:
퐶푃푈 푇푖푚푒=푆푒푐표푛푑푠/푃푟표푔푟푎푚=퐼푛푠푡푟푢푐푡푖표푛푠/푃푟표푔푟푎푚 푥 퐶푦푐푙푒푠/퐼푛푠푡푟푢푐푡푖표푛푠 푥 푆푒푐표푛푑푠/퐶푦푐푙푒
CISC minimize the number of instructions per program.
RISC does the opposite, reduce the cycles per instruction.
10. Summary.
CFD acceleration with FPGA (byteLAKE's presentation from PPAM 2019)byteLAKE
byteLAKE's presentation from the PPAM 2019 conference.
Abstract:
The goal of this work is to adapt 4 CFD kernels to the Xilinx ALVEO U250 FPGA, including first-order step of the non-linear iterative upwind advection MPDATA schemes (non-oscillatory forward in time), the divergence part of the matrix-free linear operator formulation in the iterative Krylov scheme, tridiagonal Thomas algorithm for vertical matrix inversion inside preconditioner for the iterative solver, and computation of the psuedovelocity for the second pass of upwind algorithm in MPDATA. All the kernels use 3-dimensional compute domain consisted from 7 to 11 arrays. Since all kernels belong to the group of memory bound algorithms, our main challenge is to provide the highest utilization of global memory bandwidth. Our adaptation allows us to reduce the execution time upto 4x.
Find out more at: www.byteLAKE.com/en/CFD
Foot note:
This is the presentation about the non-AI version of byteLAKE's CFD kernels, highly optimized for Alveo FPGA. Based on this research project and many others in the CFD space, we decided to shift the course of the CFD Suite product development and leverage AI to accelerate computations and enable new possibilities. Instead of adapting CFD solvers to accelerators, we use AI and work on a cross-platform solution. More on the latest: www.byteLAKE.com/en/CFDSuite.
-
Update for 2020: byteLAKE is currently developing CFD Suite as AI for CFD Suite, a collection of AI/ Artificial Intelligence Models to accelerate and enable new features for CFD simulations. It is a cross-platform solution (not only for FPGAs). More: www.byteLAKE.com/en/CFDSuite.
Various processor architectures are described in this presentation. It could be useful for people working for h/w selection and processor identification.
A Dataflow Processing Chip for Training Deep Neural Networksinside-BigData.com
In this deck from the Hot Chips conference, Chris Nicol from Wave Computing presents: A Dataflow Processing Chip for Training Deep Neural Networks.
Watch the video: https://wp.me/p3RLHQ-k6W
Learn more: https://wavecomp.ai/
and
http://www.hotchips.org/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Convolutional Neural Networks (CNNs) have achieved state-of-the-art accuracy on a variety of computer vision tasks. However, this accuracy comes at a high computational cost which is typically not met by general purpose processor.
In the recent years, different ad-hoc accelerators have been proposed to tackle this issue, and in particular, FPGAs seems to be a promising candidate for acceleration. Unfortunately, while there are many different deep learning libraries to train convolutional neural networks, creating an FPGA-based hardware accelerator is still a manual and complex task that requires time and vertical knowledge.
CONDOR is a framework to automatically derive an FPGA-based hardware accelerator starting from a high-level description of a pre-trained CNN. The goal is to make FPGA more accessible to deep learning users, offering a quick and automated way to deploy CNNs on reconfigurable hardware.
In the first part of this talk, we provide an overview of the framework and its possibilities regarding integrations with existing libraries and available deployment options, while in the second part, we dive into the architectural details of the underlying accelerator.
Convolutional Neural Networks (CNNs) have achieved state-of-the-art accuracy on a variety of computer vision tasks. However, this accuracy comes at a high computational cost which is typically not met by general purpose processor.
In the recent years, different ad-hoc accelerators have been proposed to tackle this issue, and in particular, FPGAs seems to be a promising candidate for acceleration. Unfortunately, while there are many different deep learning libraries to train convolutional neural networks, creating an FPGA-based hardware accelerator is still a manual and complex task that requires time and vertical knowledge.
CONDOR is a framework to automatically derive an FPGA-based hardware accelerator starting from a high-level description of a pre-trained CNN. The goal is to make FPGA more accessible to deep learning users, offering a quick and automated way to deploy CNNs on reconfigurable hardware.
In the first part of this talk, we provide an overview of the framework and its possibilities regarding integrations with existing libraries and available deployment options, while in the second part, we dive into the architectural details of the underlying accelerator.
AMD Bridges the X86 and ARM Ecosystems for the Data Center AMD
Presentation by Lisa Su, senior vice president and general manager, Global Business Units, AMD regarding AMD’s announcement that it will design and build 64-bit ARM technology-based processors.
This presentation was part of JNTU A 2 days workshops where IBM expert Satish presented about Open POWER ISA , Open Cores and the future architectures ..
This lesson on System-on-Chip was given for the course "Advanced Platform Architectures and Mapping Methods for Embedded Applications" at the KU Leuven and is based on chapter 8 of 'A Practical Introduction to Hardware Software Codesign (Schaumont P.)'
1. CISC VS. RISC.
2. Agenda.
3. CPU Architecture.
4. Instruction Set Architecture (ISA). Group of instructions to execute a program. Instructions are in the form of: Opcode + Operand. An agreement between hardware and human for making interaction. Example : ADD R1, R2, R3
Can be represented as :
00101111100001111001010101010101
10111010100011110101001011011010
Two major schools of ISA: CISC & RISC.
5. CISC Philosophy (Complex Instruction Set Computing). The primary goal is to complete a task in as few lines as possible. Used on PCs and laptops that need to process heavy graphics and computations. Each instruction consist of one step.
(ex: MULT 2:3, 5:2, load the two values into registers, multiplies the operands, and then stores the product in appropriate register).
6. CISC Pros & Cons. Instruction size is different from one operation to another. Operation size is smaller but no of cycles are more. Needs better hardware and powerful processing. Performance is slow due to the amount of clock time taken by different instructions.
7. RISC Philosophy (Reduced Instruction Set Computing). Use only simple instructions that can be executed within one clock cycle. Keep all instructions of same size. Allow only load/store instruction to access the memory.
(ex: MULT command divided into three separate commands:LOAD, PROD, and STORE).
8. RISC Pros & Cons. Allow free use of microprocessors space because of its simplicity. Needs large memory caches on the chip itself so require very fast memory. Give support for high level languages (like C, C++, Java). Performance depends on the programmer or compiler.
9. CPU Performance Equation. The following equation is commonly used for expressing a computer's performance ability:
퐶푃푈 푇푖푚푒=푆푒푐표푛푑푠/푃푟표푔푟푎푚=퐼푛푠푡푟푢푐푡푖표푛푠/푃푟표푔푟푎푚 푥 퐶푦푐푙푒푠/퐼푛푠푡푟푢푐푡푖표푛푠 푥 푆푒푐표푛푑푠/퐶푦푐푙푒
CISC minimize the number of instructions per program.
RISC does the opposite, reduce the cycles per instruction.
10. Summary.
CFD acceleration with FPGA (byteLAKE's presentation from PPAM 2019)byteLAKE
byteLAKE's presentation from the PPAM 2019 conference.
Abstract:
The goal of this work is to adapt 4 CFD kernels to the Xilinx ALVEO U250 FPGA, including first-order step of the non-linear iterative upwind advection MPDATA schemes (non-oscillatory forward in time), the divergence part of the matrix-free linear operator formulation in the iterative Krylov scheme, tridiagonal Thomas algorithm for vertical matrix inversion inside preconditioner for the iterative solver, and computation of the psuedovelocity for the second pass of upwind algorithm in MPDATA. All the kernels use 3-dimensional compute domain consisted from 7 to 11 arrays. Since all kernels belong to the group of memory bound algorithms, our main challenge is to provide the highest utilization of global memory bandwidth. Our adaptation allows us to reduce the execution time upto 4x.
Find out more at: www.byteLAKE.com/en/CFD
Foot note:
This is the presentation about the non-AI version of byteLAKE's CFD kernels, highly optimized for Alveo FPGA. Based on this research project and many others in the CFD space, we decided to shift the course of the CFD Suite product development and leverage AI to accelerate computations and enable new possibilities. Instead of adapting CFD solvers to accelerators, we use AI and work on a cross-platform solution. More on the latest: www.byteLAKE.com/en/CFDSuite.
-
Update for 2020: byteLAKE is currently developing CFD Suite as AI for CFD Suite, a collection of AI/ Artificial Intelligence Models to accelerate and enable new features for CFD simulations. It is a cross-platform solution (not only for FPGAs). More: www.byteLAKE.com/en/CFDSuite.
Various processor architectures are described in this presentation. It could be useful for people working for h/w selection and processor identification.
A Dataflow Processing Chip for Training Deep Neural Networksinside-BigData.com
In this deck from the Hot Chips conference, Chris Nicol from Wave Computing presents: A Dataflow Processing Chip for Training Deep Neural Networks.
Watch the video: https://wp.me/p3RLHQ-k6W
Learn more: https://wavecomp.ai/
and
http://www.hotchips.org/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Evaluating UCIe based multi-die SoC to meet timing and power Deepak Shankar
Multi-die designs allow systems engineering to pack more functionality with different timing and power constraints into a single package. Older generation multi-die split the dies into high-speed and low speed. Newer, high-performance multi-die System-on-Chip (SoC) requires interaction between memories across the die-to-die interfaces. Connections between dies must be power efficient, have low latency, provide high bandwidth to transfer massive amounts of data, and deliver error-free operation. The distribution of cores, deep neural networks and AI engines across these dies makes it extremely hard to predict the expected end-to-end latency, power spikes and effective bandwidth. Moreover, Multi-die architectures have evolved from proprietary to industry standard UCIe.
This Webinar looks at the system-wide view of performance and power in a multi-die SOC. We will be showcasing a few use cases that combines various types of processing engines across PCIe and interconnected UCIe. This modeling effort will present the user with different system performance and system architecture models and a guide on how to best bring different aspects of their design together in a holistic way that is optimized for power, timing and functionality.
During the Webinar, users can follow along using VisualSim Cloud. To get started with VisualSim Cloud, users can register and receive a login at https://www.mirabilisdesign.com/visualsim-cloud-login/. Once you receive the login, follow the instructions, and open the models provided in the Template pull-down. More instructions will be provided at the start of the Webinar.
IBM and ASTRON, the Netherlands Institute for Radio Astronomy, will unveil a prototype high-density, 64-bit microserver CPU placed on a 133 x 55 mm board running Linux. The partners are building the microserver as part of the DOME project, which is tasked with building an IT roadmap for the Square Kilometer Array, an international consortium to build the world's largest and most sensitive radio telescope. Scientists estimate that the processing power required to operate the telescope will be equal to several millions of today's fastest computers.
IBM scientist Ronald Luijten (@ronaldgadget) will present the microserver in English from ASTRON's offices in Dwingeloo, The Netherlands.
This was recorded on 3 July 14:00 Central European Time
Presentació a càrrec d'Adrián Macía (tècnic líder d'Aplicacions al CSUC) duta a terme a la "2a Jornada de formació sobre l'ús del servei de càlcul" celebrada el 19 de febrer de 2020 al CSUC.
Presentació a càrrec d'Adrián Macía, cap de Càlcul Científic del CSUC, duta a terme a la "3a Jornada de formació sobre l'ús del servei de càlcul" celebrada el 29 d'octubre de 2020 en format virtual.
A ppt on computer evolution. It show you how computers are renewed with time and how they were in past and how they are in present. Read ppt and be grateful for the knowledge you get from it
A lecture slide on the the introduction to microprocessors and microcomputers as outlined from the book Microprocessors and MIcrocomputers by John Uffenbeck
Soft computing is the use of approximate calculations to provide imprecise but usable solutions to complex computational problems. The approach enables solutions for problems that may be either unsolvable or just too time-consuming to solve with current hardware.
Object-oriented programming (OOP) is a computer programming model that organizes software design around data, or objects, rather than functions and logic. An object can be defined as a data field that has unique attributes and behavior.
OOP focuses on the objects that developers want to manipulate rather than the logic required to manipulate them. This approach to programming is well-suited for programs that are large, complex and actively updated or maintained. This includes programs for manufacturing and design, as well as mobile applications; for example, OOP can be used for manufacturing system simulation software.
Advanced computer architecture includes study of instruction set design, parallel processing, bit, instruction, and data level parallelism, distributed computing, virtualization architecture, and cloud and mobile architecture. The chapter also introduces quantum computing architecture including quantum bits, quantum gates, quantum circuits and operations, and Qiskit, a toolkit for quantum computing programming and applications. Advanced architecture for AI/ML applications is also briefly discussed.
Gain a robust foundation of management tools, crucial skills and competitive certification. Think strategically, grow better in tough markets with IIM Kozhikode. Starts Dec 30, 2022. Flexible Payment Options. IIM Kozhikode Programme. Executive Alumni Status. Learn Leadership Skills. Real-world Case Studies. Courses: Corporate Governance, Digital Transformation, Strategic Marketing.
The internet of things is a technology that allows us to add a device to an inert object (for example: vehicles, plant electronic systems, roofs, lighting, etc.) that can measure environmental parameters, generate associated data and transmit them through a communications network.
Digital Image Processing (DIP) is a software which is used to manipulate the digital images by the use of computer system. It is also used to enhance the images, to get some important information from it. For example: Adobe Photoshop, MATLAB, etc.
Reinforcement Learning (RL) is a machine learning method that empowers a specialist to learn in an intuitive environment by performing trial and error utilizing observations from its very own activities and encounters.
The client server computing works with a system of request and response. The client sends a request to the server and the server responds with the desired information. The client and server should follow a common communication protocol so they can easily interact with each other.
Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.
A distributed computer system consists of multiple software components that are on multiple computers, but run as a single system. The computers that are in a distributed system can be physically close together and connected by a local network, or they can be geographically distant and connected by a wide area network.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
It is a part of Java programming language. It is an advanced technology or advance version of Java specially designed to develop web-based, network-centric or enterprise applications. It includes the concepts like Servlet, JSP, JDBC, RMI, Socket programming, etc. It is a specialization in specific domain.
Network Security protects your network and data from breaches, intrusions and other threats. ... Network Security involves access control, virus and antivirus software, application security, network analytics, types of network-related security (endpoint, web, wireless), firewalls, VPN encryption and more.
Dynamic programming is both a mathematical optimization method and a computer programming method. The method was developed by Richard Bellman in the 1950s and has found applications in numerous fields, from aerospace engineering to economics.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Azure Interview Questions and Answers PDF By ScholarHat
Advanced Computer Architecture
1. “
”
NADAR SARASWATHI COLLEGE OF ARTS AND SCIENCE
VADAPUTHUPATTI, THENI – 625 531
Department of Computer Science and Information Technology
PRESENTED BY
NIBIYA.G
I-MSC(INFORMATION TECHONOLGY)
3. CONNECTION MACHINES
• 1981:MIT AI-lab Technical memo on CM
• 1982:Thinking Machines Inc. founded
• 1985:Danny Hillis wins ACM “Best Phn” Award
• 1986:CM-1 ships
• 1987:CM-2 ships
• 1991:CM-5 Announced
• 1991:ships
4. Cm-1 and cm-2 architecture
• Original design goal to support neuron like simulations
• Up to 64k single bit processors(actually 3 bits in and 2 out)
• 16 processors/chips, 32chips/PCD, 16 PCBs/cube, 8cubes/hypercube
• Hypercube architecture-each 16-proc chip a hyper-node
• Each proc has 4k bits of bit addressable RAM
Distributed Physical Memory
Global Memory Addresses
5. Cm-1 and cm-2 architecture
• Up to 4 front-end computer talk to sequencers via 4X4 crossbar
• “Sequencers” issues SIMD instruction over a Broadcast Network
• Bit process communication via 2D local HW grid connection(“NEWS”)
• Bit process communication via hypercube network using MSG passing
• Lots of Twinkling Lights
6.
7. CM-1 and CM-2 PROGRAMMING
• ISA support:
Bit- oriented operations
Arbitrary precision multi-bit scalar Ops using bit-serial implementation on bit process
Full multi-Dimensional Vector Ops
• “Virtual processor” idea similar to CUDA threads but they are statically allocated
• OS and programming tools run on front-ends
• List as the initial programming language
• Later c* and CM- Fortran
8. CM-2 IMPROVEMENTS
• 1 weitex IEEE FP coprocessor per 32 1-bit process
• Up to 256k bits of memory per process
• Added ECC to memory
• Implemented the IO subsystem
Up to 80 GB RAID array called “Data Vault” uses 39 Striped Disks and ECC, plus spare disks on standby
High speed graphics output
• En-route MSG combining in H-cube router
• New implementation of multi-Dimensional NEWS on top of H-cube (special addressing mode)
9.
10. CM-5 vs. CM-1 and CM-2
• Significant departure from CM-1 and CM-2
• Targeted at more scientific and business application
• More commercial off-the-shelf components (“COTS”)
• Large array of SPARC processing nodes
1-bit processors are abandoned
• Abandoned “NEWS” grid and hyper-cube networks
• Delivered 1024 node machine, with claims 16K nodes possible
• Even more Twinkling lights
11.
12. CM-5 OVERALL ARCHITECTURE
• “coordination homogeneous array of RISC processors” or “CHARM”
• Asymmetric coprocessors model
Large array of processor nodes
Small collection of control nodes
• 2 separate scalable networks
One for data
One for control and synchronization
• Still uses striped RAID for high disk Bandwidth
13. DIVISION OF LABOR
• Processor node can be assigned to a “partition”
• One control node per partition
• Control node runs scalar code, then broadcasts parallel work to processor nodes
• Processor nodes can access other node’s memory by reading or writing a global
memory address
• Processor nodes also communicate via MSG passing
• Processor nodes cannot issue system calls
14. CONTROL NODES
• Full sun workstations
• Running UNIX
• Connected to the “outside world”
• Handle partition time sharing
• Connected to both data and control networks
• Performs system diagnostics
15. PROCESSOR NODES
• Nodes are a 5-chip microprocessor
off the shelf SPARC processor @40 MHz
32MB local node memory
Multi-port memory controller for added BW
“caching techniques do not perform as well on large parallel machines”
Proprietary 4-FPU vector coprocessor
Proprietary network controller
16.
17. DATA NETWORK ARCHITECTURE
• Point to point inter-node communication and i/o
• Implemented as a fat tree
Fat tree invented by TMI employee Charles leiserson
• Claim: onsite bandwidth expandable
• Delivering 5GB/sec bisection BW on 1024 node machine
• Data router chip is a 8x8 crossbar switch
• Faulty nodes are mapped out of network
Program can not assume a network topology
• Network can be flushed when time share swaps occur
• Network, not processors, guarantee end-to-end delivery
18.
19. SEPARATE CONTROL NETWORK
• Synchronization & control network
• Complete binary tree organization
• Provides broadcast capability
• Implements barrier operations
• Implements interrupts for timesharing
• Performs reduction operators
(Sum, Max, AND, OR, Count, etc.,)
20. CM-5 PROGRAMMING
• Supports multiple parallel high level languages and programming styles
Including data parallel model from CM-1 and CM-2
• Goal: hide many decisions from programmers
CM-1, CM-2, vs. CM-5 ISA changes
Use of processor node CPU vs. vectors coprocessors
Partition wide synchronization generated by compiler
• Is it MIMD, SPMD, SIMD?
“globally synchronized MIMD”
21. SAMPLE CM APPS
• Machine learning
• VLSI design
• Geophysics (oil exploration), plate tectonics
• Fluid flow simulation
• Computer vision
• Computer graphics, animation
• Protein sequence matching
• Global climate model simulation
22. CONNECTION MACHINES
• The light panels of FROSTBURG a CM-5 on display at the
“NATION CRUPTOLOGIC MUSEM”
• The panels were used to check the usage of the processing nodes & to run
diagnostics