This document discusses parallel database management systems. It covers different architectures for parallel databases including shared memory, shared disk, and shared nothing. It also discusses techniques for parallelizing database operations like scanning, sorting, joining, and querying through techniques like partition parallelism and pipeline parallelism. The key challenges in parallel databases are data partitioning, query optimization, and transaction processing across multiple nodes.
Automatic Compilation Of MATLAB Programs For Synergistic Execution On Heterog...Sara Alvarez
This document describes a compiler called MEGHA that automatically compiles MATLAB programs to enable synergistic execution on heterogeneous processors with CPUs and GPUs. The compiler identifies data parallel regions of the MATLAB program and maps them to kernels that can execute on either the CPU or GPU. It uses graph clustering and scheduling heuristics to optimize kernel mapping and minimize data transfers between processors. The authors implemented MEGHA and found it achieved a geometric mean speedup of 19.8X over native MATLAB execution on data parallel benchmarks.
Hadoop Online Training : kelly technologies is the bestHadoop online Training Institutes in Bangalore. ProvidingHadoop online Training by real time faculty in Bangalore.
Hadoop is a software framework that allows for distributed processing of large data sets across clusters of computers. It uses MapReduce as a programming model and HDFS for storage. MapReduce divides applications into parallelizable map and reduce tasks that process key-value pairs across large datasets in a reliable and fault-tolerant manner. HDFS stores multiple replicas of data blocks for reliability and allows processing of data in parallel on nodes where the data is located. Hadoop can reliably store and process petabytes of data on thousands of low-cost commodity hardware nodes.
HIGH AVAILABILITY AND LOAD BALANCING FOR POSTGRESQL DATABASES: DESIGNING AND ...ijdms
The aim of the paper is to design and implement an approach that provides high availability and load balancing to PostgreSQL databases. Shared nothing architecture and replicated centralized middleware architecture were selected in order to achieve data replication and load balancing among database servers. Pgpool-II was used to implementing these architectures. Besides, taking advantage of pgpool-II as
a framework, several scripts were developed in Bash for restoration of corrupted databases. In order to avoid single point of failure Linux HA (Heartbeat and Pacemaker) was used, whose responsibility is monitoring all processes involved in the whole solution. As a result applications can operate with a more reliable PostgreSQL database server, the most suitable situation is for applications with more load of reading statement (typically select) over writing statement (typically update). Also approach presented is only intended for Linux based Operating System.
This document provides an introduction to distributed systems including definitions, characteristics, motivation, and models. It discusses key topics such as message passing vs shared memory, synchronous vs asynchronous execution, and challenges in distributed system design. Models of distributed computation and logical time frameworks are also introduced.
This document discusses challenges and opportunities in parallel graph processing for big data. It describes how graphs are ubiquitous but processing large graphs at scale is difficult due to their huge size, complex correlations between data entities, and skewed distributions. Current computation models have problems with ghost vertices, too much interaction between partitions, and lack of support for iterative graph algorithms. New frameworks are needed to handle these graphs in a scalable way with low memory usage and balanced computation and communication.
Hadoop is a software framework that allows for distributed processing of large data sets across clusters of computers. It uses MapReduce and HDFS to parallelize tasks, distribute data storage, and provide fault tolerance. Applications of Hadoop include log analysis, data mining, and machine learning using large datasets at companies like Yahoo!, Facebook, and The New York Times.
1) Parallelism can significantly speed up data access and queries by dividing problems across multiple machines. For example, scanning 1TB in parallel across 1000 machines would take 1.5 minutes instead of 1.2 days with a single machine.
2) There are two main types of parallelism in database management systems - pipeline parallelism where each machine performs one step in a process, and partition parallelism where each machine works on a different portion of data.
3) Shared-nothing architectures where each machine has its own processors, memory and disks are best for parallel databases as they are cheap, scalable, and allow each machine to work independently on partitioned data.
Automatic Compilation Of MATLAB Programs For Synergistic Execution On Heterog...Sara Alvarez
This document describes a compiler called MEGHA that automatically compiles MATLAB programs to enable synergistic execution on heterogeneous processors with CPUs and GPUs. The compiler identifies data parallel regions of the MATLAB program and maps them to kernels that can execute on either the CPU or GPU. It uses graph clustering and scheduling heuristics to optimize kernel mapping and minimize data transfers between processors. The authors implemented MEGHA and found it achieved a geometric mean speedup of 19.8X over native MATLAB execution on data parallel benchmarks.
Hadoop Online Training : kelly technologies is the bestHadoop online Training Institutes in Bangalore. ProvidingHadoop online Training by real time faculty in Bangalore.
Hadoop is a software framework that allows for distributed processing of large data sets across clusters of computers. It uses MapReduce as a programming model and HDFS for storage. MapReduce divides applications into parallelizable map and reduce tasks that process key-value pairs across large datasets in a reliable and fault-tolerant manner. HDFS stores multiple replicas of data blocks for reliability and allows processing of data in parallel on nodes where the data is located. Hadoop can reliably store and process petabytes of data on thousands of low-cost commodity hardware nodes.
HIGH AVAILABILITY AND LOAD BALANCING FOR POSTGRESQL DATABASES: DESIGNING AND ...ijdms
The aim of the paper is to design and implement an approach that provides high availability and load balancing to PostgreSQL databases. Shared nothing architecture and replicated centralized middleware architecture were selected in order to achieve data replication and load balancing among database servers. Pgpool-II was used to implementing these architectures. Besides, taking advantage of pgpool-II as
a framework, several scripts were developed in Bash for restoration of corrupted databases. In order to avoid single point of failure Linux HA (Heartbeat and Pacemaker) was used, whose responsibility is monitoring all processes involved in the whole solution. As a result applications can operate with a more reliable PostgreSQL database server, the most suitable situation is for applications with more load of reading statement (typically select) over writing statement (typically update). Also approach presented is only intended for Linux based Operating System.
This document provides an introduction to distributed systems including definitions, characteristics, motivation, and models. It discusses key topics such as message passing vs shared memory, synchronous vs asynchronous execution, and challenges in distributed system design. Models of distributed computation and logical time frameworks are also introduced.
This document discusses challenges and opportunities in parallel graph processing for big data. It describes how graphs are ubiquitous but processing large graphs at scale is difficult due to their huge size, complex correlations between data entities, and skewed distributions. Current computation models have problems with ghost vertices, too much interaction between partitions, and lack of support for iterative graph algorithms. New frameworks are needed to handle these graphs in a scalable way with low memory usage and balanced computation and communication.
Hadoop is a software framework that allows for distributed processing of large data sets across clusters of computers. It uses MapReduce and HDFS to parallelize tasks, distribute data storage, and provide fault tolerance. Applications of Hadoop include log analysis, data mining, and machine learning using large datasets at companies like Yahoo!, Facebook, and The New York Times.
1) Parallelism can significantly speed up data access and queries by dividing problems across multiple machines. For example, scanning 1TB in parallel across 1000 machines would take 1.5 minutes instead of 1.2 days with a single machine.
2) There are two main types of parallelism in database management systems - pipeline parallelism where each machine performs one step in a process, and partition parallelism where each machine works on a different portion of data.
3) Shared-nothing architectures where each machine has its own processors, memory and disks are best for parallel databases as they are cheap, scalable, and allow each machine to work independently on partitioned data.
This document provides an introduction and overview of Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop uses MapReduce and HDFS to parallelize workloads and store data redundantly across nodes to solve issues around hardware failure and combining results. Key aspects covered include how HDFS distributes and replicates data, how MapReduce isolates processing into mapping and reducing functions to abstract communication, and how Hadoop moves computation to the data to improve performance.
Presentation on Large Scale Data ManagementChris Bunch
The document summarizes recent research on MapReduce and virtual machine migration. It discusses papers that compare MapReduce to parallel databases, describe techniques for live migration of virtual machines with low downtime, and propose using system call logging and replay to further reduce migration times and overhead. The document provides context on debates around MapReduce and outlines key approaches and findings from several papers on virtual machine migration.
This document summarizes a project report on optimizing fracking simulations for GPU acceleration. The simulations model hydraulic fracturing and consist of three phases. The focus was on the second phase, which calculates interaction factors and stresses between grid cells and takes 80% of the CPU execution time. This phase was implemented on a GPU using techniques like finding parallelism at the cell and grid level, optimizing data transfers, memory access, and using streams to execute cells concurrently. These optimizations led to speedups of up to 56x compared to the CPU implementation.
Big data refers to large volumes of unstructured or semi-structured data that is difficult to process using traditional databases and analysis tools. The amount of data generated daily is growing exponentially due to factors like increased internet usage and data collection by organizations. Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses HDFS for reliable storage and MapReduce as a programming model to process data in parallel across nodes.
Modified Pure Radix Sort for Large Heterogeneous Data Set IOSR Journals
The document presents a modified pure radix sort algorithm for sorting large heterogeneous data sets. It discusses problems with traditional radix sort algorithms and previous work optimizing radix sort. The proposed algorithm divides the data into numeric and string clusters. It then distributes the numeric data into subsets of equal length which are sorted in parallel using an approach that bypasses certain digits in each pass. String data is sorted by assigning numbers to identical strings. The algorithm is tested on two machines and shows improved performance over traditional radix sort and quicksort, providing sorting times 10-20% faster for large heterogeneous datasets.
Scaling Application on High Performance Computing Clusters and Analysis of th...Rusif Eyvazli
The document discusses techniques for scaling applications across computing nodes in high performance computing (HPC) clusters. It analyzes the performance of different computing nodes on various applications like BLASTX, HPL, and JAGS. Array job facilities are used to parallelize applications by dividing iterations into independent tasks assigned across nodes. Python programs are created to analyze system performance based on log files and produce plots showing differences in node performance on different applications. The plots help with preventative maintenance and capacity management of the HPC system.
The document proposes a Modified Pure Radix Sort algorithm for large heterogeneous datasets. The algorithm divides the data into numeric and string processes that work simultaneously. The numeric process further divides data into sublists by element length and sorts them simultaneously using an even/odd logic across digits. The string process identifies common patterns to convert strings to numbers that are then sorted. This optimizes problems with traditional radix sort through a distributed computing approach.
The document discusses big data and distributed computing. It explains that big data refers to large, unstructured datasets that are too large for traditional databases. Distributed computing uses multiple computers connected via a network to process large datasets in parallel. Hadoop is an open-source framework for distributed computing that uses MapReduce and HDFS for parallel processing and storage across clusters. HDFS stores data redundantly across nodes for fault tolerance.
This document discusses consistency models in distributed systems with replication. It describes reasons for replication including reliability and performance. Various consistency models are covered, including: strict consistency where reads always return the most recent write; sequential consistency where operations appear in a consistent order across processes; weak consistency which enforces consistency on groups of operations; and release consistency which separates acquiring and releasing locks to selectively guard shared data. Client-centric models like eventual consistency are also discussed, where updates gradually propagate to all replicas.
This document discusses several memory management techniques:
1. Contiguous allocation allocates processes to contiguous regions of memory but can lead to fragmentation.
2. Paging divides memory into pages and processes into page tables to map virtual to physical addresses, reducing fragmentation. It uses translation lookaside buffers (TLBs) to speed address translation.
3. Segmentation divides processes into logical segments and uses segment tables to map segments to physical addresses. It provides a modular view of memory but external fragmentation remains an issue.
This document discusses different memory management techniques including:
1. Contiguous allocation allocates processes to contiguous regions of memory but can lead to fragmentation. Paging and segmentation address this by allowing non-contiguous allocation.
2. Paging maps logical addresses to physical frames through a page table. It supports non-contiguous allocation but has translation overhead that is reduced using translation lookaside buffers.
3. Segmentation divides memory into logical segments and uses a segment table to map logical to physical addresses. It matches the user's view of memory but external fragmentation remained an issue until combined with paging.
This document discusses several memory management techniques:
1. Contiguous allocation allocates processes to contiguous regions of memory but can lead to fragmentation.
2. Paging divides memory into pages and processes into page tables to map virtual to physical addresses, reducing fragmentation. It uses translation lookaside buffers (TLBs) to speed address translation.
3. Segmentation divides processes into logical segments and uses segment tables to map segments to physical addresses. It provides a modular view of memory but external fragmentation remains an issue.
Jumpstart your career with the world’s most in-demand technology: Hadoop. Hadooptrainingacademy provides best Hadoop online training with quality videos, comprehensive
online live training and detailed study material. Join today!
For more info, visit: http://www.hadooptrainingacademy.com/
Contact Us:
8121660088
732-419-2619
http://www.hadooptrainingacademy.com/
The document discusses parallel and distributed databases. It begins by defining a centralized database and explaining why parallel databases are needed due to increasing data sizes and faster access requirements. It then covers the benefits of parallel databases like improved response time and throughput. Different architectures for parallel databases are described including shared memory, shared disk, and shared nothing. Key techniques for parallelizing operations like loading, scanning, sorting and joins are outlined. Data partitioning methods like range, hash and round robin are also summarized. Finally, the two phase commit protocol for distributed transactions is briefly explained.
The document discusses big data and distributed computing. It provides examples of the large amounts of data generated daily by organizations like the New York Stock Exchange and Facebook. It explains how distributed computing frameworks like Hadoop use multiple computers connected via a network to process large datasets in parallel. Hadoop's MapReduce programming model and HDFS distributed file system allow users to write distributed applications that process petabytes of data across commodity hardware clusters.
At improve digital we collect and store large volumes of machine generated and behavioural data from our fleet of ad servers. For some time we have performed mostly batch processing through a data warehouse that combines traditional RDBMs (MySQL), columnar stores (Infobright, impala+parquet) and Hadoop.
We wish to share our experiences in enhancing this capability with systems and techniques that process the data as streams in near-realtime. In particular we will cover:
• The architectural need for an approach to data collection and distribution as a first-class capability
• The different needs of the ingest pipeline required by streamed realtime data, the challenges faced in building these pipelines and how they forced us to start thinking about the concept of production-ready data.
• The tools we used, in particular Apache Kafka as the message broker, Apache Samza for stream processing and Apache Avro to allow schema evolution; an essential element to handle data whose formats will change over time.
• The unexpected capabilities enabled by this approach, including the value in using realtime alerting as a strong adjunct to data validation and testing.
• What this has meant for our approach to analytics and how we are moving to online learning and realtime simulation.
This is still a work in progress at Improve Digital with differing levels of production-deployed capability across the topics above. We feel our experiences can help inform others embarking on a similar journey and hopefully allow them to learn from our initiative in this space.
This document provides an outline for the course CS-416 Parallel and Distributed Systems. It discusses key topics like parallel computing concepts, architectures, algorithms, and programming environments. Parallel computing involves using multiple compute resources simultaneously by breaking a problem into discrete parts that can execute concurrently on different processors. The main types of parallel processes are sequential and parallel. Parallelism is useful for solving huge complex problems faster using techniques like decomposition, data parallelism, and task parallelism. Popular parallel programming environments include MPI, OpenMP, and hybrid models.
The Concept of Load Balancing Server in Secured and Intelligent NetworkIJAEMSJORNAL
Hundreds and thousands of data packets are routed every second by computer networks which are complex systems. The data should be routed efficiently to handle large amounts of data in network. A core networking solution which is responsible for distribution of incoming traffic among servers hosting the same content is load balancing. For example, if there are ten servers within a network and two of them are doing 95% of the work, the network is not running very efficiently. If each server was handling about 10% of the traffic, the network would run much faster.Networks get more efficient with the help of Load balancing. The traffic is evenly distributed amongst the network making sure no single device is overwhelmed.When a request is balanced across multiple servers, it prevents any server from becoming a single point of failure. It improves overall availability and responsiveness. To evenly split the traffic load among several different servers web servers; often use load balancing.Load balancing requires hardware or software that divides incoming traffic amongst the available serverseither it is done on a local network or a large web server. High amount of traffic is received by a network that have one server dedicated to balance the load among other servers and devices in the network. This server is often known as load balancer. Load balancing is used by clusters or multiple computers that work together, to spread out processing jobs among the available systems.
Hadoop is a software framework that allows for distributed processing of large data sets across clusters of computers. It includes MapReduce for distributed computing, HDFS for storage, and runs efficiently on large clusters by distributing data and processing across nodes. Example applications include log analysis, machine learning, and sorting 1TB of data in under a minute. It is fault-tolerant, scalable, and designed for processing vast amounts of data in a reliable and cost-effective manner.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
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This document provides an introduction and overview of Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop uses MapReduce and HDFS to parallelize workloads and store data redundantly across nodes to solve issues around hardware failure and combining results. Key aspects covered include how HDFS distributes and replicates data, how MapReduce isolates processing into mapping and reducing functions to abstract communication, and how Hadoop moves computation to the data to improve performance.
Presentation on Large Scale Data ManagementChris Bunch
The document summarizes recent research on MapReduce and virtual machine migration. It discusses papers that compare MapReduce to parallel databases, describe techniques for live migration of virtual machines with low downtime, and propose using system call logging and replay to further reduce migration times and overhead. The document provides context on debates around MapReduce and outlines key approaches and findings from several papers on virtual machine migration.
This document summarizes a project report on optimizing fracking simulations for GPU acceleration. The simulations model hydraulic fracturing and consist of three phases. The focus was on the second phase, which calculates interaction factors and stresses between grid cells and takes 80% of the CPU execution time. This phase was implemented on a GPU using techniques like finding parallelism at the cell and grid level, optimizing data transfers, memory access, and using streams to execute cells concurrently. These optimizations led to speedups of up to 56x compared to the CPU implementation.
Big data refers to large volumes of unstructured or semi-structured data that is difficult to process using traditional databases and analysis tools. The amount of data generated daily is growing exponentially due to factors like increased internet usage and data collection by organizations. Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses HDFS for reliable storage and MapReduce as a programming model to process data in parallel across nodes.
Modified Pure Radix Sort for Large Heterogeneous Data Set IOSR Journals
The document presents a modified pure radix sort algorithm for sorting large heterogeneous data sets. It discusses problems with traditional radix sort algorithms and previous work optimizing radix sort. The proposed algorithm divides the data into numeric and string clusters. It then distributes the numeric data into subsets of equal length which are sorted in parallel using an approach that bypasses certain digits in each pass. String data is sorted by assigning numbers to identical strings. The algorithm is tested on two machines and shows improved performance over traditional radix sort and quicksort, providing sorting times 10-20% faster for large heterogeneous datasets.
Scaling Application on High Performance Computing Clusters and Analysis of th...Rusif Eyvazli
The document discusses techniques for scaling applications across computing nodes in high performance computing (HPC) clusters. It analyzes the performance of different computing nodes on various applications like BLASTX, HPL, and JAGS. Array job facilities are used to parallelize applications by dividing iterations into independent tasks assigned across nodes. Python programs are created to analyze system performance based on log files and produce plots showing differences in node performance on different applications. The plots help with preventative maintenance and capacity management of the HPC system.
The document proposes a Modified Pure Radix Sort algorithm for large heterogeneous datasets. The algorithm divides the data into numeric and string processes that work simultaneously. The numeric process further divides data into sublists by element length and sorts them simultaneously using an even/odd logic across digits. The string process identifies common patterns to convert strings to numbers that are then sorted. This optimizes problems with traditional radix sort through a distributed computing approach.
The document discusses big data and distributed computing. It explains that big data refers to large, unstructured datasets that are too large for traditional databases. Distributed computing uses multiple computers connected via a network to process large datasets in parallel. Hadoop is an open-source framework for distributed computing that uses MapReduce and HDFS for parallel processing and storage across clusters. HDFS stores data redundantly across nodes for fault tolerance.
This document discusses consistency models in distributed systems with replication. It describes reasons for replication including reliability and performance. Various consistency models are covered, including: strict consistency where reads always return the most recent write; sequential consistency where operations appear in a consistent order across processes; weak consistency which enforces consistency on groups of operations; and release consistency which separates acquiring and releasing locks to selectively guard shared data. Client-centric models like eventual consistency are also discussed, where updates gradually propagate to all replicas.
This document discusses several memory management techniques:
1. Contiguous allocation allocates processes to contiguous regions of memory but can lead to fragmentation.
2. Paging divides memory into pages and processes into page tables to map virtual to physical addresses, reducing fragmentation. It uses translation lookaside buffers (TLBs) to speed address translation.
3. Segmentation divides processes into logical segments and uses segment tables to map segments to physical addresses. It provides a modular view of memory but external fragmentation remains an issue.
This document discusses different memory management techniques including:
1. Contiguous allocation allocates processes to contiguous regions of memory but can lead to fragmentation. Paging and segmentation address this by allowing non-contiguous allocation.
2. Paging maps logical addresses to physical frames through a page table. It supports non-contiguous allocation but has translation overhead that is reduced using translation lookaside buffers.
3. Segmentation divides memory into logical segments and uses a segment table to map logical to physical addresses. It matches the user's view of memory but external fragmentation remained an issue until combined with paging.
This document discusses several memory management techniques:
1. Contiguous allocation allocates processes to contiguous regions of memory but can lead to fragmentation.
2. Paging divides memory into pages and processes into page tables to map virtual to physical addresses, reducing fragmentation. It uses translation lookaside buffers (TLBs) to speed address translation.
3. Segmentation divides processes into logical segments and uses segment tables to map segments to physical addresses. It provides a modular view of memory but external fragmentation remains an issue.
Jumpstart your career with the world’s most in-demand technology: Hadoop. Hadooptrainingacademy provides best Hadoop online training with quality videos, comprehensive
online live training and detailed study material. Join today!
For more info, visit: http://www.hadooptrainingacademy.com/
Contact Us:
8121660088
732-419-2619
http://www.hadooptrainingacademy.com/
The document discusses parallel and distributed databases. It begins by defining a centralized database and explaining why parallel databases are needed due to increasing data sizes and faster access requirements. It then covers the benefits of parallel databases like improved response time and throughput. Different architectures for parallel databases are described including shared memory, shared disk, and shared nothing. Key techniques for parallelizing operations like loading, scanning, sorting and joins are outlined. Data partitioning methods like range, hash and round robin are also summarized. Finally, the two phase commit protocol for distributed transactions is briefly explained.
The document discusses big data and distributed computing. It provides examples of the large amounts of data generated daily by organizations like the New York Stock Exchange and Facebook. It explains how distributed computing frameworks like Hadoop use multiple computers connected via a network to process large datasets in parallel. Hadoop's MapReduce programming model and HDFS distributed file system allow users to write distributed applications that process petabytes of data across commodity hardware clusters.
At improve digital we collect and store large volumes of machine generated and behavioural data from our fleet of ad servers. For some time we have performed mostly batch processing through a data warehouse that combines traditional RDBMs (MySQL), columnar stores (Infobright, impala+parquet) and Hadoop.
We wish to share our experiences in enhancing this capability with systems and techniques that process the data as streams in near-realtime. In particular we will cover:
• The architectural need for an approach to data collection and distribution as a first-class capability
• The different needs of the ingest pipeline required by streamed realtime data, the challenges faced in building these pipelines and how they forced us to start thinking about the concept of production-ready data.
• The tools we used, in particular Apache Kafka as the message broker, Apache Samza for stream processing and Apache Avro to allow schema evolution; an essential element to handle data whose formats will change over time.
• The unexpected capabilities enabled by this approach, including the value in using realtime alerting as a strong adjunct to data validation and testing.
• What this has meant for our approach to analytics and how we are moving to online learning and realtime simulation.
This is still a work in progress at Improve Digital with differing levels of production-deployed capability across the topics above. We feel our experiences can help inform others embarking on a similar journey and hopefully allow them to learn from our initiative in this space.
This document provides an outline for the course CS-416 Parallel and Distributed Systems. It discusses key topics like parallel computing concepts, architectures, algorithms, and programming environments. Parallel computing involves using multiple compute resources simultaneously by breaking a problem into discrete parts that can execute concurrently on different processors. The main types of parallel processes are sequential and parallel. Parallelism is useful for solving huge complex problems faster using techniques like decomposition, data parallelism, and task parallelism. Popular parallel programming environments include MPI, OpenMP, and hybrid models.
The Concept of Load Balancing Server in Secured and Intelligent NetworkIJAEMSJORNAL
Hundreds and thousands of data packets are routed every second by computer networks which are complex systems. The data should be routed efficiently to handle large amounts of data in network. A core networking solution which is responsible for distribution of incoming traffic among servers hosting the same content is load balancing. For example, if there are ten servers within a network and two of them are doing 95% of the work, the network is not running very efficiently. If each server was handling about 10% of the traffic, the network would run much faster.Networks get more efficient with the help of Load balancing. The traffic is evenly distributed amongst the network making sure no single device is overwhelmed.When a request is balanced across multiple servers, it prevents any server from becoming a single point of failure. It improves overall availability and responsiveness. To evenly split the traffic load among several different servers web servers; often use load balancing.Load balancing requires hardware or software that divides incoming traffic amongst the available serverseither it is done on a local network or a large web server. High amount of traffic is received by a network that have one server dedicated to balance the load among other servers and devices in the network. This server is often known as load balancer. Load balancing is used by clusters or multiple computers that work together, to spread out processing jobs among the available systems.
Hadoop is a software framework that allows for distributed processing of large data sets across clusters of computers. It includes MapReduce for distributed computing, HDFS for storage, and runs efficiently on large clusters by distributing data and processing across nodes. Example applications include log analysis, machine learning, and sorting 1TB of data in under a minute. It is fault-tolerant, scalable, and designed for processing vast amounts of data in a reliable and cost-effective manner.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
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Parallel Database description in database management
1. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 1
Parallel DBMS
Slides by Joe Hellerstein, UCB, with some material from Jim Gray, Microsoft
Research, and some modifications of mine. See also:
http://www.research.microsoft.com/research/BARC/Gray/PDB95.ppt
Chapter 22, Sections 22.1–22.6
2. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 2
Why Parallel Access To Data?
1 Terabyte
10 MB/s
At 10 MB/s
1.2 days to scan
1 Terabyte
1,000 x parallel
1.5 minute to scan.
Parallelism:
divide a big problem
into many smaller ones
to be solved in parallel.
3. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 3
Parallel DBMS: Introduction
Centralized DBMSs assume that
– Processing of individual transactions is sequential
– Control and/or data are maintained at one single site
These assumptions have been relaxed in recent decades:
– Parallel DBMSs:
Use of parallel evaluation techniques; parallelization of various operations
such as data loading, index construction, and query evaluations.
Data may still be centralized; distribution dictated solely by performance
considerations
– Distributed DBMSs:
Use of both control and data distribution; data and control are dispersed
and stored across several sites
Data distribution also dictated by considerations of increased availability
and local ownership
4. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 6
Architectures for Parallel Databases
Shared Memory
(SMP)
Shared Disk Shared Nothing
(network)
CLIENTS CLIENTS
CLIENTS
Memory
Processors
Easy to program
Expensive to build
Difficult to scaleup
Hard to program
Cheap to build
Easy to scaleup
Sequent, SGI, Sun VMScluster, Sysplex Tandem, Teradata, SP2
5. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 7
Some Parallelism Terminology
Speed-Up
– For a given amount of data,
more resources (CPUs)
means proportionally more
transactions processed per
second.
Scale-Up with DB size
– If resources increased in
proportion to increase in
data size, # of trans./sec.
remains constant.
# of CPUs
#
of
trans./sec.
(throughput)
Ideal
# of CPUS, DB size
#
of
trans./sec.
(throughput)
Ideal
6. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 8
Some Parallelism Terminology
Scale-Up
– If resources increased in
proportion to increase in
# of trans./sec., response
time remains constant.
# of CPUS, # trans./sec.
#
of
sec./Trans.
(response
time)
Ideal
7. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 9
What Systems Work Which Way ?
Shared Nothing
Teradata: 400 nodes
Tandem: 110 nodes
IBM / SP2 / DB2: 128 nodes
Informix/SP2 48 nodes
ATT & Sybase ? nodes
Shared Disk
Oracle 170 nodes
DEC Rdb 24 nodes
Shared Memory
Informix 9 nodes
RedBrick ? nodes
CLIENTS
Memory
Processors
CLIENTS
CLIENTS
(as of 9/1995)
8. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 10
Different Types of DBMS Parallelisms
Intra-operator parallelism
– get all machines working to compute a given
operation (scan, sort, join)
Inter-operator parallelism
– each operator may run concurrently on a different
site (exploits pipelining)
Inter-query parallelism
– different queries run on different sites
We’ll focus on intra-operator parallelism
9. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 11
Automatic Data Partitioning
Partitioning a table:
Range Hash Round Robin
Shared disk and memory less sensitive to partitioning,
Shared nothing benefits from "good" partitioning
A...E F...J K...N O...S T...Z A...E F...J K...N O...S T...Z A...E F...J K...N O...S T...Z
Good for equijoins,
range queries,
Selections, group-by
Good for equijoins Good to spread load
10. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 12
Parallelizing Existing Code for
Evaluating a Relational Operator
How to readily parallelize existing code to enable
sequential evaluation of a relational operator?
– Idea: use parallel data streams
– Details:
MERGE streams from different disks or the output of
other operators to provide input streams for an operator
SPLIT output of an operator to parallelize subsequent
processing
A parallel evaluation plan is a dataflow network
of relational, merge, and split operators.
– Merge and split should have buffering capabilities
– They should regulate the output of relational operators
11. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 13
Parallel Scanning and Bulk Loading
Scanning
– Pages of a relation are read in parallel, and, if the
relation is partitioned across many disks, retrieved
tuples are merged.
– Selection of tuples matching some condition may
not require all sites if range or hash partitioning is
used.
Bulk loading:
– Indexes can be built at each partition.
– Sorting of data entries required for building the
indexes during bulk loading can be done in
parallel.
12. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 14
Parallel Sorting
Simple idea:
– Let each CPU sorts the portion of
the relation located on its local disk;
– then, merge the sorted sets of tuples
Better idea:
– First scan in parallel and redistribute the relation by range-
partitioning all tuples; then each processor sorts its tuples:
The CPU collects tuples until memory is full
It sorts the in-memory tuples and writes out a run, until all incoming
tuples have been written to sorted runs on disk
The runs on disk are then merged to get the sorted version of the
portion of the relation assigned to the CPU
Retrieve the entire sorted relation by visiting the CPUs in the range-
order to scan the tuples.
– Problem: how to avoid skew in range-partition!
– Solution: “sample” the data at start and sort the sample to
determine partition points (splitting vector).
13. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 16
Parallel Joins: Range-Partition
Assumptions: A and B are initially distributed across
many processors, and k processors are availoable.
Algorithm to join relations A and B on attribute age:
1. At each processor where there are subsets of A and/or B,
divide the range of age into k disjoint subranges and place
partition A and B tuples into partitions corresponding to
the subranges.
2. Assign each partition to a processor to carry out a local
join. Each processor joins the A and B tuples assigned to it.
3. Tuples scanned from each processor are split, with a split
operator, into output streams, depending on how many
processors are available for parallel joins.
4. Each join process receives input streams of A and B tuples
from several processors, with merge operators merging all
inputs streams from A and B, respectively.
14. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 17
Parallel Joins: Hash-Partitions
Algorithm for hash-partition: Step 1 on
previous slide changes to:
1. At each processor where there are subsets of A and/or B,
all local tuples are retrieved and hashed on the age
attribute into k disjoint partitions, with the same hash
function h used at all sites.
Using range partitioning leads to a parallel
version of Sort-Merge Join.
Using hash partitioning leads to a parallel
version of Hash Join.
15. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 18
Parallel Hash Join
In first phase, partitions get distributed to
different sites:
– A good hash function automatically distributes
work evenly!
Do second phase at each site.
Almost always the winner for equi-join.
Original Relations
(R then S)
OUTPUT
2
B main memory buffers Disk
Disk
INPUT
1
hash
function
h
B-1
Partitions
1
2
B-1
. . .
Phase
1
16. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 19
Dataflow Network for Parallel Join
Good use of split/merge makes it easier to
build parallel versions of sequential join code.
17. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 20
Improved Parallel Hash Join
Assumptions:
A and B are very large, which leads to the size of
each partition still being too large, which in turns
leads to high local cost for processing the
“smaller” joins.
k partitions, n processors and k=n.
Idea: Execute all smaller joins of Ai and Bi,
i=1,…,k, one after the other, with each join
executed in parallel using all processors.
18. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 21
Improved Parallel Hash Join (Cont’d)
Algorithm:
1. At each processor, apply hash function h1 to
partition A and B into partitions i=1,…,k . Suppose
|A|<|B|. Then choose k such sum of all k partitions
of A fits into aggregate memory of all n processors.
2. For i=1,…,k , process join of i-th partitions of A and B
by doing this at every site:
1. Apply 2nd hash function h2 to all Ai tuples to determine
where they should be joined send t to site h2(t).
2. Add in-coming Ai tuples to an in-memory hash table.
3. Apply h2 to all Bi tuples to determine where they should be
joined send t to site h2(t).
4. Probe the in-memory hash table with in-coming Bi tuples.
19. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 22
Parallel Query Optimization
Complex Queries: Inter-Operator parallelism
– Pipelining between operators:
note that sort and phase 1 of hash-join block the
pipeline!!
– Bushy Trees
A B R S
Sites 1-4 Sites 5-8
Sites 1-8
20. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 23
Parallel Query Optimization (Cont’d)
Common approach: 2 phases
– Pick best sequential plan (System R algorithm)
– Pick degree of parallelism based on current
system parameters.
“Bind” operators to processors
– Take query tree, “decorate” as in previous picture.
21. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 24
Parallel DBMS Summary
Parallelism natural to query processing:
– Both pipeline and partition parallelism!
Shared-Nothing vs. Shared-Mem
– Shared-disk too, but less standard
– Shared-mem easy, costly. Doesn’t scaleup.
– Shared-nothing cheap, scales well, harder to
implement.
Intra-op, Inter-op, & Inter-query parallelism
all possible.
22. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 25
Parallel DBMS Summary (Cont’d)
Data layout choices important!
Most DB operations can be done with
partition-parallelism
– Sort.
– Sort-merge join, hash-join.
Complex plans.
– Allow for pipeline-parallelism, but sorts, hashes
block the pipeline.
– Partition parallelism achieved via bushy trees.
23. Database Management Systems, 2nd Edition. Raghu Ramakrishnan and Johannes Gehrke 26
Parallel DBMS Summary (Cont’d)
Hardest part of the equation: optimization.
– 2-phase optimization simplest, but can be
ineffective.
– More complex schemes still at the research stage.
We haven’t said anything about transactions,
logging.
– Easy in shared-memory architecture.
– Takes some care in shared-nothing.