The VINEYARD project aims to increase the performance and energy efficiency of data centers through the use of heterogeneous hardware accelerators like programmable dataflow engines and FPGA-accelerated servers. The project will develop these novel accelerators and integrate them into the data center infrastructure with an open programming framework and runtime scheduler. This will allow big data applications to leverage the accelerators while hiding the complexity from programmers. The goals are demonstrated through applications in computational neuroscience, finance, data analytics, and IoT.
In this deck, the Radio Free HPC team looks at the future of Operating Systems in the new world of computing.
Listen to the Podcast: http://www.radiofreehpc.com/wp/?p=686
Cloudgene - A MapReduce based Workflow Management SystemLukas Forer
Cloudgene is a freely available platform to improve the usability of MapReduce programs by providing a graphical user interface for the execution, the import and export of data and the reproducibility of workflows on in-house (private clouds) and rented clusters (public clouds).
MAP-REDUCE IMPLEMENTATIONS: SURVEY AND PERFORMANCE COMPARISONijcsit
Map Reduce has gained remarkable significance as a rominent parallel data processing tool in the research community, academia and industry with the spurt in volume of data that is to be analyzed. Map Reduce is used in different applications such as data mining, data analytic where massive data analysis is required, but still it is constantly being explored on different parameters such as performance and efficiency. This survey intends to explore large scale data processing using Map Reduce and its various implementations to facilitate the database, researchers and other communities in developing the technical understanding of the Map Reduce framework. In this survey, different Map Reduce implementations are explored and their inherent features are compared on different parameters. It also addresses the open issues and challenges raised on fully functional DBMS/Data Warehouse on Map Reduce. The comparison of various Map Reduce implementations is done with the most popular implementation Hadoop and other similar implementations using other platforms.
Managing and Deploying High Performance Computing Clusters using Windows HPC ...Saptak Sen
The new management features built into Windows HPC Server 2008 R2 are the foundation for deploying and managing HPC clusters of scale up to 1000 nodes. Join us for a deep dive in monitoring and diagnostic tools, a review of the updated heat-map and template-based deployment. We also cover the new PowerShell-based scripting capabilities: the basics of management shell, as well as the underlying design and key concepts, new Reporting Capabilities, and a discussion on network boot.
Enhanced Data Visualization provided for 200,000 Machines with OpenTSDB and C...YASH Technologies
YASH tuned applications and databases to maximize system performance, distributed the storage of monitored data, and eliminated destructive down-sampling.
Serhii Kholodniuk: What you need to know, before migrating data platform to G...Lviv Startup Club
Serhii Kholodniuk: What you need to know, before migrating data platform to GCP (Google cloud platform)
AI & BigData Online Day 2022
Website: https://aiconf.com.ua
Youtube: https://www.youtube.com/startuplviv
FB: https://www.facebook.com/aiconf
In this deck, the Radio Free HPC team looks at the future of Operating Systems in the new world of computing.
Listen to the Podcast: http://www.radiofreehpc.com/wp/?p=686
Cloudgene - A MapReduce based Workflow Management SystemLukas Forer
Cloudgene is a freely available platform to improve the usability of MapReduce programs by providing a graphical user interface for the execution, the import and export of data and the reproducibility of workflows on in-house (private clouds) and rented clusters (public clouds).
MAP-REDUCE IMPLEMENTATIONS: SURVEY AND PERFORMANCE COMPARISONijcsit
Map Reduce has gained remarkable significance as a rominent parallel data processing tool in the research community, academia and industry with the spurt in volume of data that is to be analyzed. Map Reduce is used in different applications such as data mining, data analytic where massive data analysis is required, but still it is constantly being explored on different parameters such as performance and efficiency. This survey intends to explore large scale data processing using Map Reduce and its various implementations to facilitate the database, researchers and other communities in developing the technical understanding of the Map Reduce framework. In this survey, different Map Reduce implementations are explored and their inherent features are compared on different parameters. It also addresses the open issues and challenges raised on fully functional DBMS/Data Warehouse on Map Reduce. The comparison of various Map Reduce implementations is done with the most popular implementation Hadoop and other similar implementations using other platforms.
Managing and Deploying High Performance Computing Clusters using Windows HPC ...Saptak Sen
The new management features built into Windows HPC Server 2008 R2 are the foundation for deploying and managing HPC clusters of scale up to 1000 nodes. Join us for a deep dive in monitoring and diagnostic tools, a review of the updated heat-map and template-based deployment. We also cover the new PowerShell-based scripting capabilities: the basics of management shell, as well as the underlying design and key concepts, new Reporting Capabilities, and a discussion on network boot.
Enhanced Data Visualization provided for 200,000 Machines with OpenTSDB and C...YASH Technologies
YASH tuned applications and databases to maximize system performance, distributed the storage of monitored data, and eliminated destructive down-sampling.
Serhii Kholodniuk: What you need to know, before migrating data platform to G...Lviv Startup Club
Serhii Kholodniuk: What you need to know, before migrating data platform to GCP (Google cloud platform)
AI & BigData Online Day 2022
Website: https://aiconf.com.ua
Youtube: https://www.youtube.com/startuplviv
FB: https://www.facebook.com/aiconf
Evolution of Distributed computing: Scalable computing over the Internet – Technologies for network based systems – clusters of cooperative computers - Grid computing Infrastructures – cloud computing - service oriented architecture – Introduction to Grid Architecture and standards – Elements of Grid – Overview of Grid Architecture.
MapR Technologies Chief Marketing Officer, Jack Norris, talks about the advantages of Hadoop. He elaborates and multiple use cases and explains how MapR Technologies is the best Hadoop distribution.
Enhanced Data Visualization provided for 200,000 Machines with OpenTSDB and ...YASH Technologies
During the implementation of OpenTSDB, YASH tuned applications and databases to maximize system performance,
distributed the storage of monitored data, and eliminated destructive down-sampling.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiDataWorks Summit
Cybersecurity requires an organization to collect data, analyze it, and alert on cyber anomalies in near real-time. This is a challenging endeavor when considering the variety of data sources which need to be collected and analyzed. Everything from application logs, network events, authentications systems, IOT devices, business events, cloud service logs, and more need to be taken into consideration. In addition, multiple data formats need to be transformed and conformed to be understood by both humans and ML/AI algorithms.
To solve this problem, the Aetna Global Security team developed the Unified Data Platform based on Apache NiFi, which allows them to remain agile and adapt to new security threats and the onboarding of new technologies in the Aetna environment. The platform currently has over 60 different data flows with 95% doing real-time ETL and handles over 20 billion events per day. In this session learn from Aetna’s experience building an edge to AI high-speed data pipeline with Apache NiFi.
BUILDING A PRIVATE HPC CLOUD FOR COMPUTE AND DATA-INTENSIVE APPLICATIONSijccsa
Traditional HPC (High Performance Computing) clusters are best suited for well-formed calculations. The
orderly batch-oriented HPC cluster offers maximal potential for performance per application, but limits
resource efficiency and user flexibility. An HPC cloud can host multiple virtual HPC clusters, giving the
scientists unprecedented flexibility for research and development. With the proper incentive model,
resource efficiency will be automatically maximized. In this context, there are three new challenges. The
first is the virtualization overheads. The second is the administrative complexity for scientists to manage
the virtual clusters. The third is the programming model. The existing HPC programming models were
designed for dedicated homogeneous parallel processors. The HPC cloud is typically heterogeneous and
shared. This paper reports on the practice and experiences in building a private HPC cloud using a subset
of a traditional HPC cluster. We report our evaluation criteria using Open Source software, and
performance studies for compute-intensive and data-intensive applications. We also report the design and
implementation of a Puppet-based virtual cluster administration tool called HPCFY. In addition, we show
that even if the overhead of virtualization is present, efficient scalability for virtual clusters can be achieved
by understanding the effects of virtualization overheads on various types of HPC and Big Data workloads.
We aim at providing a detailed experience report to the HPC community, to ease the process of building a
private HPC cloud using Open Source software.
The existing concept of virtualization provides increased system utilization via virtual infrastructure and promotes resource
sharing across an organization. To maximize the effective use of resources, cloud computing is used which uses service oriented architecture
infrastructure with on demand provisioning of software, platform and infrastructure as service. Dynamic service management is achieved by
implementing the cloud where dynamically scalable resources are provided as a service over the internet. This can be viewed as an
extension of grid computing, combined with utility computing and autonomic computing which helps an organization in converting the
capital expenditure into utility expenditure. This paper focuses on the basics of cloud computing technology.
Evolution of Distributed computing: Scalable computing over the Internet – Technologies for network based systems – clusters of cooperative computers - Grid computing Infrastructures – cloud computing - service oriented architecture – Introduction to Grid Architecture and standards – Elements of Grid – Overview of Grid Architecture.
MapR Technologies Chief Marketing Officer, Jack Norris, talks about the advantages of Hadoop. He elaborates and multiple use cases and explains how MapR Technologies is the best Hadoop distribution.
Enhanced Data Visualization provided for 200,000 Machines with OpenTSDB and ...YASH Technologies
During the implementation of OpenTSDB, YASH tuned applications and databases to maximize system performance,
distributed the storage of monitored data, and eliminated destructive down-sampling.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiDataWorks Summit
Cybersecurity requires an organization to collect data, analyze it, and alert on cyber anomalies in near real-time. This is a challenging endeavor when considering the variety of data sources which need to be collected and analyzed. Everything from application logs, network events, authentications systems, IOT devices, business events, cloud service logs, and more need to be taken into consideration. In addition, multiple data formats need to be transformed and conformed to be understood by both humans and ML/AI algorithms.
To solve this problem, the Aetna Global Security team developed the Unified Data Platform based on Apache NiFi, which allows them to remain agile and adapt to new security threats and the onboarding of new technologies in the Aetna environment. The platform currently has over 60 different data flows with 95% doing real-time ETL and handles over 20 billion events per day. In this session learn from Aetna’s experience building an edge to AI high-speed data pipeline with Apache NiFi.
BUILDING A PRIVATE HPC CLOUD FOR COMPUTE AND DATA-INTENSIVE APPLICATIONSijccsa
Traditional HPC (High Performance Computing) clusters are best suited for well-formed calculations. The
orderly batch-oriented HPC cluster offers maximal potential for performance per application, but limits
resource efficiency and user flexibility. An HPC cloud can host multiple virtual HPC clusters, giving the
scientists unprecedented flexibility for research and development. With the proper incentive model,
resource efficiency will be automatically maximized. In this context, there are three new challenges. The
first is the virtualization overheads. The second is the administrative complexity for scientists to manage
the virtual clusters. The third is the programming model. The existing HPC programming models were
designed for dedicated homogeneous parallel processors. The HPC cloud is typically heterogeneous and
shared. This paper reports on the practice and experiences in building a private HPC cloud using a subset
of a traditional HPC cluster. We report our evaluation criteria using Open Source software, and
performance studies for compute-intensive and data-intensive applications. We also report the design and
implementation of a Puppet-based virtual cluster administration tool called HPCFY. In addition, we show
that even if the overhead of virtualization is present, efficient scalability for virtual clusters can be achieved
by understanding the effects of virtualization overheads on various types of HPC and Big Data workloads.
We aim at providing a detailed experience report to the HPC community, to ease the process of building a
private HPC cloud using Open Source software.
The existing concept of virtualization provides increased system utilization via virtual infrastructure and promotes resource
sharing across an organization. To maximize the effective use of resources, cloud computing is used which uses service oriented architecture
infrastructure with on demand provisioning of software, platform and infrastructure as service. Dynamic service management is achieved by
implementing the cloud where dynamically scalable resources are provided as a service over the internet. This can be viewed as an
extension of grid computing, combined with utility computing and autonomic computing which helps an organization in converting the
capital expenditure into utility expenditure. This paper focuses on the basics of cloud computing technology.
Using Platform-As-A-Service (Paas) for Better Resource Utilization and Better...AM Publications
Popularity of cloud computing has increased many times in the last few years. One major driving force
behind this rapid increase in adoption of cloud is the economic benefits that the cloud provides. The benefits imply the
economies of scale that go with the pool of configurable computing resources which together constitute the cloud.
Cloud frees the user from the job of setting up and maintaining the computational infrastructure and helps him to
focus on developing and perfecting his application. Also the cloud provides the benefit of scaling (manual/real-time)
so that the application continues to work even under heavy load. However moving onto cloud is not an easy process
and requires planning. In this paper we review some techniques that have been used or proposed by research scholars
and cloud experts to create customized cloud platforms. These techniques can be used to design our own cloud
infrastructure to enable us to reap the benefits that cloud computing has to provide.
MapR 5.2: Getting More Value from the MapR Converged Data PlatformMapR Technologies
End of maintenance for MapR 4.x is coming in January, so now is a good time to plan your upgrade. Please join us to learn about the recent developments during the past year in the MapR Platform that will make the upgrade effort this year worthwhile.
This lecture aims to give some food for thought regarding how the current High Performance Computing systems (hardware and software) tends to merge with Big Data ones (Machine Learning, Analytics and Enterprise workloads) in order to meet both workloads demands sharing the same clusters.
Privacy preserving public auditing for secured cloud storagedbpublications
As the cloud computing technology develops during the last decade, outsourcing data to cloud service for storage becomes an attractive trend, which benefits in sparing efforts on heavy data maintenance and management. Nevertheless, since the outsourced cloud storage is not fully trustworthy, it raises security concerns on how to realize data deduplication in cloud while achieving integrity auditing. In this work, we study the problem of integrity auditing and secure deduplication on cloud data. Specifically, aiming at achieving both data integrity and deduplication in cloud, we propose two secure systems, namely SecCloud and SecCloud+. SecCloud introduces an auditing entity with a maintenance of a MapReduce cloud, which helps clients generate data tags before uploading as well as audit the integrity of data having been stored in cloud. Compared with previous work, the computation by user in SecCloud is greatly reduced during the file uploading and auditing phases. SecCloud+ is designed motivated by the fact that customers always want to encrypt their data before uploading, and enables integrity auditing and secure deduplication on encrypted data.
How to scale your PaaS with OVH infrastructure?OVHcloud
ForePaaS has developed an “as-a-service” platform which lets you automate an infrastructure designed for analytical applications. The company has formed a cloud partnership with OVH in order to deliver flexible solutions for containerised and high-performance tools, such as Kunernetes and Docker.
Apache Big_Data Europe event: "Demonstrating the Societal Value of Big & Smar...BigData_Europe
H2020 BigDataEurope is a flagship project of the European Union's Horizon 2020 framework programme for research and innovation. In this talk we present the Docker-based BigDataEurope platform, which integrates a variety of Big Data processing components such as Hive, Cassandra, Apache Flink and Spark. Particularly supporting the variety dimension of Big Data, it adds a semantic data processing layer, which allows to ingest, map, transform and exploit semantically enriched data. In this talk, we will present the innovative technical architecture as well as applications of the BigDataEurope platform for life sciences (OpenPhacts), mobility, food & agriculture as well as industrial analytics (predictive maintenance). We demonstrate how societal value can be generated by Big Data analytics, e.g. making transportation networks more efficient or facilitating drug research.
Calista Redmond from IBM presented this deck at the Switzerland HPC Conference.
“The OpenPOWER Foundation was founded in 2013 as an open technical membership organization that will enable data centers to rethink their approach to technology. Today, nearly 200 member companies are enabled to customize POWER CPU processors and system platforms for optimization and innovation for their business needs. These innovations include custom systems for large or warehouse scale data centers, workload acceleration through GPU, FPGA or advanced I/O, platform optimization for SW appliances, or advanced hardware technology exploitation. OpenPOWER members are actively pursing all of these innovations and more and welcome all parties to join in moving the state of the art of OpenPOWER systems design forward.”
Watch the video presentation: http://insidehpc.com/2016/03/openpower-foundation/
See more talks in the Swiss Conference Video Gallery: http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Performance Improvement of Cloud Computing Data Centers Using Energy Efficien...IJAEMSJORNAL
Cloud computing is a technology that provides a platform for the sharing of resources such as software, infrastructure, application and other information. It brings a revolution in Information Technology industry by offering on-demand of resources. Clouds are basically virtualized datacenters and applications offered as services. Data center hosts hundreds or thousands of servers which comprised of software and hardware to respond the client request. A large amount of energy requires to perform the operation.. Cloud Computing is facing lot of challenges like Security of Data, Consumption of energy, Server Consolidation, etc. The research work focuses on the study of task scheduling management in a cloud environment. The main goal is to improve the performance (resource utilization and redeem the consumption of energy) in data centers. Energy-efficient scheduling of workloads helps to redeem the consumption of energy in data centers, thus helps in better usage of resource. This is further reducing operational costs and provides benefits to the clients and also to cloud service provider. In this abstract of paper, the task scheduling in data centers have been compared. Cloudsim a toolkit for modeling and simulation of cloud computing environment has been used to implement and demonstrate the experimental results. The results aimed at analyzing the energy consumed in data centers and shows that by having reduce the consumption of energy the cloud productivity can be improved.
WSO2 Data Analytics Server is a comprehensive enterprise data analytics platform; it fuses batch and real-time analytics of any source of data with predictive analytics via machine learning.
Internet of Things A Vision, Architectural Elements, and Future Directions Mostafa Arjmand
Present paper aboat Internet of Things (IoT) A Vision, Architectural Elements, and Future Directions
Overall IoT vision and the technologies that will achieve the it
Application domains in IoT with a new approach in defining them
Cloud centric Internet of Things realization and challenges
Case study of data analytics on the Aneka/Azure cloud platform
Open Challenges and Future Directions
Smart environment application domains
Cloud computing
Cloud centric Internet of Things
Microsoft Azure
ALT-F1.BE : The Accelerator (Google Cloud Platform)Abdelkrim Boujraf
The Accelerator is an IT infrastructure able to collect and analyze a massive amount of public data on the WWW.
The Accelerator leverages the untapped potential of web data with the first solution designed for diverse sectors,
completely scalable, available on-premise, and cloud-provider agnostic.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Top 10 Oil and Gas Projects in Saudi Arabia 2024.pdf
VINEYARD Overview - ARC 2016
1. VINEYARD project:
Versatile Integrated framework for
Accelerator-based Heterogeneous
Data Centres
International Symposium of Applied Reconfigurable Computing, March 2016
Christoforos Kachris, Dimitrios Soudris
ICCS/NTUA, Greece
1
2. Current Data Centers
By 2018, more than three quarters (78%) of workloads will be processed
by cloud data centers.
International Symposium of Applied Reconfigurable Computing, March 2016
[Source: Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update 2014–2019 White Paper]
2
3. Power budget has reached its limit
The power budget per processor has reached its limit. We can increase the number of
cores but we can no longer power all of the processors at the same time.
International Symposium of Applied Reconfigurable Computing, March 2016
[Source: HiPEAC Vision 2015]
3
4. The data deluge gap
The Moore’s law cannot follow the increased growth of the data traffic that needs to be processed.
International Symposium of Applied Reconfigurable Computing, March 2016 4
5. Power consumption of Data Centers
• Currently Data
Centers
consume huge
amounts of
energy
• Servers
consume
around 30% of
the total power
budget
International Symposium of Applied Reconfigurable Computing, March 2016 5
6. Hardware accelerators
International Symposium of Applied Reconfigurable Computing, March 2016
• HW acceleration can be used to reduce significantly the
execution time and the energy consumption of several
applications (10x-100x)
6
7. FPGAs in the Cloud
International Symposium of Applied Reconfigurable Computing, March 2016
• Altera’s acquisition by Intel
• Microsoft’s catapult for Bing search
• IBM open power – CAPI interface with FPGAs
7
8. Heterogeneous DCs for energy
efficiency
International Symposium of Applied Reconfigurable Computing, March 2016
“The only way to differentiate server offerings is through accelerators, like we saw with cell
phones”, OpenServer Summit 2014 Leendert Van Doorn; AMD
TODAY’s DCs Future Heterogeneous DCs
with VINEYARD infrastructure
Run-time manager and
orchestrator
3rd party HW
accelerators
Run-time scheduler
Big Data Applications
• Low performance
• High power consumption
• Best effort
• Higher performance
• Lower power consumption
• Predictable performance
Requirements
Servers
P
Servers
P
P
P
P
P
P
P
P
P
P
P
DFE
DFE
DFE
DFE
VINEYARD
Servers with
dataflow-based
accelerators (DFE)
Big Data Applications
8
9. VINEYARD’s goals
VINEYARD aims to:
• Build an integrated platform for energy-efficient data
centres based on novel programmable hardware
accelerators (i.e. Dataflow engines and FPGA-coupled
servers).
• Develop a high-level programming framework and
big data infrastructure for allowing end-users to
seamlessly utilize these accelerators in
heterogeneous computing systems by employing
typical cloud programming frameworks (i.e. Spark).
The main goal is to increase significantly the performance
and the energy efficiency of the data centers
International Symposium of Applied Reconfigurable Computing, March 2016 9
10. VINEYARD accelerators
VINEYARD will develop two types of hardware
accelerators:
• Dataflow engines: These accelerators will be
mainly used for applications that can be
represented mainly as data-flow graphs
• FPGA-based engines: These servers will be based
on MPSoC FPGA that incorporate multiple 64-bit
ARM cores and will be used for application that
needs low latency communication between the
processors and the accelerator
International Symposium of Applied Reconfigurable Computing, March 2016 10
11. VINEYARD Heterogeneous
Accelerators-based Data centre
International Symposium of Applied Reconfigurable Computing, March 2016
Bioinformatics Finance
Big Data Applications
VINEYARD Progr. Framework
Synthesis
(OpenSPL,OpenCL)
Pattern
Matching
Analytics
engines
String
matching
Other
processing
Commonly used
Function/tasks
…
HW Manager
Library of Hardware
functions as IP Blocks
Requirements:
• Throughput
• Latency
• Power
Racks with programmable
dataflow engine (DFE)
accelerators
Server Racks with
commodity processor
Repository
Compressi
on
EncryptionScheduler
DFE
DFE
DFE
DFE
Cluster Resource Manager
Analytics
P
P
P
P
P
P
P
P
Programm
able
Logic
Racks with
MPSoC FPGAs
Programming Framework, APIs
11
12. Objectives
• Objective 1: Development of novel Programmable
Dataflow Engines (DFE) for servers: One of the main
objectives of VINEYARD will be the development of novel
programmable dataflow engines (hardware accelerators)
based on coarse-grain programmable components that
can be coupled to servers’ processor in heterogeneous
data centres.
• Objective 2: Development of novel FPGA-accelerated
servers: VINEYARD will develop novel server blades that
will be based on high performance and energy-efficient
FPGAs that incorporate multiple low power cores.
International Symposium of Applied Reconfigurable Computing, March 2016 12
13. Objectives
• Objective 3: Development of an open-source integrated
programming framework that can be used for the
programming of heterogeneous systems consisting of
general purpose processors (CPUs), and accelerators
(programmable dataflow engines and FPGAs) based on
traditional cloud programming frameworks (i.e. Spark).
• Objective 4: Development of a run-time
scheduler/orchestrator that controls the utilization of the
accelerators based on the applications’ requirements
(execution time, power consumption, available resources,
etc.).
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14. Objectives
• Objective 5: Development of a novel Virtual-Machine
(VM) appliance model for provisioning of data to shared
accelerators. Targeting cloud deployments, this VINEYARD
effort will bring both tangible and novel results. The
enhanced VINEYARD middleware augments the
functionality of the orchestrator, by enabling more
informed allocation of tasks to accelerators.
• Objective 6: Ecosystem Establishment and Support. The
establishment of an ecosystem that will empower open
innovation based on hardware accelerators as data-centre
plugins, thereby facilitating innovative enterprises (large
industries, SMEs, and creative start-ups) to develop novel
solutions using VINEYARDS’s leading edge developments.
International Symposium of Applied Reconfigurable Computing, March 2016 14
15. Overview of VINEYARD
Overall VINEYARD aspires to address the open challenges in integrating
programmable and hardware accelerators to the predominant
software stacks used for data analytics in the Cloud:
1. hide the accelerator from the programmer by presenting it as a
pure library function, embeddable in query processing, data
processing or aggregation tasks, and by extension to analytical
libraries written on top of high-level programming models;
2. extend the runtime systems of high-level analytics languages to
handle efficiently scheduling, communication, and synchronization
with programmable accelerators; and
3. improve the performance robustness of analytics written in high-
level languages against artefacts of virtualization, notably
performance interference due to contention on shared resources
and hidden noise in hypervisors and hosting VMs.
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16. Consortium
International Symposium of Applied Reconfigurable Computing, March 2016
Platform Evaluator
Data Centre Vendor
System Vendor (Dataflow Engines)
System Software
Programming framework &
Hardware accelerators
Data Centre
Software developers
Data Centre End User
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17. The VINEYARD value-chain
International Symposium of Applied Reconfigurable Computing, March 2016
VINEYARD framework
Soft IP-cores
vendor
Heterogeneous
Platform
Application
developers,
Cloud tenants
End user -
client
IP1
IP3
IP4
IP2
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18. Three real-world scenarios
The VINEYARD project will be demonstrated on three real-word
applications:
• Computational neuroscience (Neurasmus):
• high-accuracy simulation of the Olivocerebellar system of the
brain, crucial to the understanding of brain functionality
• Financial applications (Neurocom Lux and ATHEX):
• Trading system operations
• Pre-trade risk management
• Data analytics (LeanXcale)
• TPC-C (on-line transaction processing (OLTP) benchmark)
• TPC-H (decision support benchmark).
• IoT application (Linear Road will also be used as a representative
workload in IoT applications)
International Symposium of Applied Reconfigurable Computing, March 2016 18
20. Thank you.
More information on www.vineyard-h2020.eu
Contact details:
Prof. Dimitrios Soudris: dsoudris@microlab.ntua.gr
Dr. Christoforos Kachris: kachris@microlab.ntua.gr
International Symposium of Applied Reconfigurable Computing, March 2016
This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 687628
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