This document provides an introduction and overview of the Memoria application specific data structures toolkit. It discusses how modern hardware limitations require data structures optimized for sequential access over random access in memory hierarchies. It summarizes the motivation and goals of Memoria, which separates logical and physical data representations to optimize for specific access patterns. Key features of Memoria include a balanced search tree, dynamic vector, and vector map data structures that achieve high performance through compact in-memory representations.
MULTI-CORE PROCESSORS: CONCEPTS AND IMPLEMENTATIONSijcsit
This research paper aims at comparing two multi-core processors machines, the Intel core i7-4960X
processor (Ivy Bridge E) and the AMD Phenom II X6. It starts by introducing a single-core processor machine to motivate the need for multi-core processors. Then, it explains the multi-core processor machine and the issues that rises in implementing them. It also provides a real life example machines such as
TILEPro64 and Epiphany-IV 64-core 28nm Microprocessor (E64G401). The methodology that was used in comparing the Intel core i7 and AMD phenom II processors starts by explaining how processors' performance are measured, then by listing the most important and relevant technical specification to the
comparison. After that, running the comparison by using different metrics such as power, the use of HyperThreading
technology, the operating frequency, the use of AES encryption and decryption, and the different characteristics of cache memory such as the size, classification, and its memory controller. Finally, reaching to a roughly decision about which one of them has a better over all performance.
Non-shared disk clusters provide a fault tolerant and cost-effective approach to data-intensive computing. This document describes a prototype non-shared disk cluster and plans for a full implementation. The prototype uses local disks on nodes that are not shared over the network, requiring processing to occur on nodes containing the needed data. A file catalog tracks data placement. The full implementation will include modifications to analysis software to dispatch jobs to nodes based on file location. Fault tolerance is provided by restarting jobs if nodes fail and restoring failed nodes.
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.
This document summarizes a research paper that proposes a new density-based clustering technique called Triangle-Density Based Clustering Technique (TDCT) to efficiently cluster large spatial datasets. TDCT uses a polygon approach where the number of data points inside each triangle of a polygon is calculated to determine triangle densities. Triangle densities are used to identify clusters based on a density confidence threshold. The technique aims to identify clusters of arbitrary shapes and densities while minimizing computational costs. Experimental results demonstrate the technique's superiority in terms of cluster quality and complexity compared to other density-based clustering algorithms.
The primary reasons for using parallel computing:
Save time - wall clock time
Solve larger problems
Provide concurrency (do multiple things at the same time)
This document summarizes a research paper on developing an improved LEACH (Low-Energy Adaptive Clustering Hierarchy) communication protocol for energy efficient data mining in multi-feature sensor networks. It begins with background on wireless sensor networks and issues like energy efficiency. It then discusses the existing LEACH protocol and its drawbacks. The proposed improved LEACH protocol includes cluster heads, sub-cluster heads, and cluster nodes to address LEACH's limitations. This new version aims to minimize energy consumption during cluster formation and data aggregation in multi-feature sensor networks.
The document describes iDedup, a system for performing inline data deduplication on primary storage systems while minimizing performance impacts. It leverages two insights about duplicated data in real-world workloads: 1) spatial locality exists where duplicated data occurs in sequences of disk blocks, and 2) temporal locality exists where duplicated data is accessed repeatedly close in time. The system performs selective deduplication of block sequences to reduce fragmentation and seeks during reads. It also replaces expensive on-disk deduplication metadata with a smaller in-memory fingerprint cache to reduce writes. Evaluation shows the system achieves 60-70% deduplication with less than 5% CPU overhead and 2-4% increased latency.
This document provides an introduction and overview of the Memoria application specific data structures toolkit. It discusses how modern hardware limitations require data structures optimized for sequential access over random access in memory hierarchies. It summarizes the motivation and goals of Memoria, which separates logical and physical data representations to optimize for specific access patterns. Key features of Memoria include a balanced search tree, dynamic vector, and vector map data structures that achieve high performance through compact in-memory representations.
MULTI-CORE PROCESSORS: CONCEPTS AND IMPLEMENTATIONSijcsit
This research paper aims at comparing two multi-core processors machines, the Intel core i7-4960X
processor (Ivy Bridge E) and the AMD Phenom II X6. It starts by introducing a single-core processor machine to motivate the need for multi-core processors. Then, it explains the multi-core processor machine and the issues that rises in implementing them. It also provides a real life example machines such as
TILEPro64 and Epiphany-IV 64-core 28nm Microprocessor (E64G401). The methodology that was used in comparing the Intel core i7 and AMD phenom II processors starts by explaining how processors' performance are measured, then by listing the most important and relevant technical specification to the
comparison. After that, running the comparison by using different metrics such as power, the use of HyperThreading
technology, the operating frequency, the use of AES encryption and decryption, and the different characteristics of cache memory such as the size, classification, and its memory controller. Finally, reaching to a roughly decision about which one of them has a better over all performance.
Non-shared disk clusters provide a fault tolerant and cost-effective approach to data-intensive computing. This document describes a prototype non-shared disk cluster and plans for a full implementation. The prototype uses local disks on nodes that are not shared over the network, requiring processing to occur on nodes containing the needed data. A file catalog tracks data placement. The full implementation will include modifications to analysis software to dispatch jobs to nodes based on file location. Fault tolerance is provided by restarting jobs if nodes fail and restoring failed nodes.
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.
This document summarizes a research paper that proposes a new density-based clustering technique called Triangle-Density Based Clustering Technique (TDCT) to efficiently cluster large spatial datasets. TDCT uses a polygon approach where the number of data points inside each triangle of a polygon is calculated to determine triangle densities. Triangle densities are used to identify clusters based on a density confidence threshold. The technique aims to identify clusters of arbitrary shapes and densities while minimizing computational costs. Experimental results demonstrate the technique's superiority in terms of cluster quality and complexity compared to other density-based clustering algorithms.
The primary reasons for using parallel computing:
Save time - wall clock time
Solve larger problems
Provide concurrency (do multiple things at the same time)
This document summarizes a research paper on developing an improved LEACH (Low-Energy Adaptive Clustering Hierarchy) communication protocol for energy efficient data mining in multi-feature sensor networks. It begins with background on wireless sensor networks and issues like energy efficiency. It then discusses the existing LEACH protocol and its drawbacks. The proposed improved LEACH protocol includes cluster heads, sub-cluster heads, and cluster nodes to address LEACH's limitations. This new version aims to minimize energy consumption during cluster formation and data aggregation in multi-feature sensor networks.
The document describes iDedup, a system for performing inline data deduplication on primary storage systems while minimizing performance impacts. It leverages two insights about duplicated data in real-world workloads: 1) spatial locality exists where duplicated data occurs in sequences of disk blocks, and 2) temporal locality exists where duplicated data is accessed repeatedly close in time. The system performs selective deduplication of block sequences to reduce fragmentation and seeks during reads. It also replaces expensive on-disk deduplication metadata with a smaller in-memory fingerprint cache to reduce writes. Evaluation shows the system achieves 60-70% deduplication with less than 5% CPU overhead and 2-4% increased latency.
This document discusses parallel programming concepts including threads, synchronization, and barriers. It defines parallel programming as carrying out many calculations simultaneously. Advantages include increased computational power and speed up. Key issues in parallel programming are sharing resources between threads, and ensuring synchronization through locks and barriers. Data parallel programming is discussed where the same operation is performed on different data elements simultaneously.
詹剑锋:Big databench—benchmarking big data systemshdhappy001
This document discusses BigDataBench, an open source project for big data benchmarking. BigDataBench includes six real-world data sets and 19 workloads that cover common big data applications and preserve the four V's of big data. The workloads were chosen to represent typical application domains like search engines, social networks, and e-commerce. BigDataBench aims to provide a standardized benchmark for evaluating big data systems, architectures, and software stacks. It has been used in several case studies for workload characterization and evaluating the performance and energy efficiency of different hardware platforms for big data workloads.
The document discusses types of parallelism in hardware, software, and applications. It covers parallel architectures like multicore processors and clusters. Flynn's taxonomy classifies computers based on instruction and data streams as SISD, SIMD, MISD, and MIMD. Memory models include shared memory and message passing. Examples show parallelization of an equation solver kernel using instruction-level, task-level, and data-level parallelism. Speedup metrics like problem-constrained and time-constrained scaling are also introduced.
Intel's Nehalem Microarchitecture by Glenn Hintonparallellabs
Intel's Nehalem family of CPUs span from large multi-socket 32 core/64 thread systems to ultra small form factor laptops. What were some of the key tradeoffs in architecting and developing the Nehalem family of CPUs? What pipeline should it use? Should it optimize for servers? For desktops? For Laptops? There are lots of tradeoffs here. This talk will discuss some of the tradeoffs and results.
The document discusses different types of parallelism that can be utilized in parallel database systems: I/O parallelism to retrieve relations from multiple disks in parallel, interquery parallelism to run different queries simultaneously, intraquery parallelism to parallelize operations within a single query, and intraoperation parallelism to parallelize individual operations like sort and join. It also covers techniques for partitioning relations across disks and handling skew to balance the workload.
Run-Time Adaptive Processor Allocation of Self-Configurable Intel IXP2400 Net...CSCJournals
An ideal Network Processor, that is, a programmable multi-processor device must be capable of offering both the flexibility and speed required for packet processing. But current Network Processor systems generally fall short of the above benchmarks due to traffic fluctuations inherent in packet networks, and the resulting workload variation on individual pipeline stage over a period of time ultimately affects the overall performance of even an otherwise sound system. One potential solution would be to change the code running at these stages so as to adapt to the fluctuations; a near robust system with standing traffic fluctuations is the dynamic adaptive processor, reconfiguring the entire system, which we introduce and study to some extent in this paper. We achieve this by using a crucial decision making model, transferring the binary code to the processor through the SOAP protocol.
This document discusses parallel computing. It begins by defining parallel processing as using simultaneous data processing tasks to save time and/or money and solve larger problems. It then discusses how parallel computing uses multiple compute resources simultaneously to solve computational problems. Some examples of parallel phenomena in nature and technology are provided. The document outlines several areas where parallel computing is applied, including physics, bioscience, and computer science. It discusses the benefits of parallel computing in saving time and money and solving larger problems too large for a single computer. Finally, it briefly mentions ways to classify parallel computers and some basic requirements for achieving parallel execution.
This document discusses storage management in database systems. It describes the storage device hierarchy from fastest but smallest (cache) to slowest but largest (magnetic tapes). It covers main memory, hard disks, solid state drives and tertiary storage. The document also discusses RAID configurations and how the relational model is represented on secondary storage through records, blocks, files and indexes.
An asynchronous replication model to improve data available into a heterogene...Alexander Decker
This document summarizes a research paper that proposes an asynchronous replication model to improve data availability in heterogeneous systems. The proposed model uses a loosely coupled architecture between main and replication servers to reduce dependencies. It also supports heterogeneous systems, allowing different parts of an application to run on different systems for better performance. This makes it a cost-effective solution for data replication across different system types.
This document provides an overview of parallel and distributed systems. It discusses that a parallel computer contains multiple processing elements that communicate and cooperate to solve problems quickly, while a distributed system contains independent computers that appear as a single system. It notes that parallel computers are implicitly distributed systems. It then discusses reasons for using parallel and distributed computing like Moore's law and limitations of sequential processing due to power and latency walls. Finally, it outlines some topics that will be covered in the course like different parallel computing platforms, programming paradigms, and challenges in parallel and distributed systems.
1. File allocation methods include contiguous, linked, and indexed allocation. Contiguous allocation stores files in contiguous blocks but can lead to fragmentation. Linked allocation stores non-contiguous blocks through pointers but has overhead. Indexed allocation uses index blocks to point to data blocks.
2. File systems manage free space through data structures like bit vectors, linked lists, and space maps. Bit vectors require extra space but allow contiguous allocation. Linked lists have no wasted space but non-contiguous allocation. Space maps divide devices into metaslabs for efficient free space management.
3. Performance depends on allocation algorithms, metadata handling, buffer caching, and write policies. Techniques like read-ahead and free-behind optimize sequential access,
The document provides an overview of parallel processing and multiprocessor systems. It discusses Flynn's taxonomy, which classifies computers as SISD, SIMD, MISD, or MIMD based on whether they process single or multiple instructions and data in parallel. The goals of parallel processing are to reduce wall-clock time and solve larger problems. Multiprocessor topologies include uniform memory access (UMA) and non-uniform memory access (NUMA) architectures.
This document discusses various applications of parallel processing. It describes how parallel processing is used in numeric weather prediction to forecast weather by processing large amounts of observational data. It is also used in oceanography and astrophysics to study oceans and conduct particle simulations. Other applications mentioned include socioeconomic modeling, finite element analysis, artificial intelligence, seismic exploration, genetic engineering, weapon research, medical imaging, remote sensing, energy exploration, and more. The document also discusses loosely coupled and tightly coupled multiprocessors and the differences between the two approaches.
This document discusses using distributed processing to increase computational power. It introduces the concepts of using a distributed file system like HDFS to store large files across multiple machines. MapReduce is presented as a way to process large amounts of data in parallel by splitting it into chunks, mapping functions to each chunk, and then recombining the results. Hadoop is introduced as an open-source MapReduce framework that uses HDFS for storage and allows processing of data across a cluster of machines. Examples are given of writing Map and Reduce functions and running jobs on Hadoop to demonstrate distributed processing of large datasets.
The document compares the performance of two in-memory data platforms, Hazelcast and Infinispan, based on benchmarks of read and write operations using different numbers of load producing threads and server nodes. Key findings include that Hazelcast had faster average read and write times than Infinispan, lower response time deviations, and higher throughput rates, especially with 4 server nodes. Hazelcast also used system resources like CPU and memory more efficiently.
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
This document describes PowerAlluxio, an in-memory file system that improves on Alluxio by enabling shared memory utilization across cluster nodes while maintaining memory locality. PowerAlluxio allows client nodes to utilize remote node memory if local memory is full, improving cluster memory usage without sacrificing performance. It also introduces a new Smart LRU eviction policy that reduces elapsed time by 24.76% for large datasets. Experiments showed PowerAlluxio achieved up to 14.11x faster task completion times compared to Alluxio when data could be fully cached.
Distributed Framework for Data Mining As a Service on Private CloudIJERA Editor
Data mining research faces two great challenges: i. Automated mining ii. Mining of distributed data.
Conventional mining techniques are centralized and the data needs to be accumulated at central location. Mining
tool needs to be installed on the computer before performing data mining. Thus, extra time is incurred in
collecting the data. Mining is 4 done by specialized analysts who have access to mining tools. This technique is
not optimal when the data is distributed over the network. To perform data mining in distributed scenario, we
need to design a different framework to improve efficiency. Also, the size of accumulated data grows
exponentially with time and is difficult to mine using a single computer. Personal computers have limitations in
terms of computation capability and storage capacity.
Cloud computing can be exploited for compute-intensive and data intensive applications. Data mining
algorithms are both compute and data intensive, therefore cloud based tools can provide an infrastructure for
distributed data mining. This paper is intended to use cloud computing to support distributed data mining. We
propose a cloud based data mining model which provides the facility of mass data storage along with distributed
data mining facility. This paper provide a solution for distributed data mining on Hadoop framework using an
interface to run the algorithm on specified number of nodes without any user level configuration. Hadoop is
configured over private servers and clients can process their data through common framework from anywhere in
private network. Data to be mined can either be chosen from cloud data server or can be uploaded from private
computers on the network. It is observed that the framework is helpful in processing large size data in less time
as compared to single system.
Parallel processing architectures allow for simultaneous computation across multiple processing elements. There are four main types of parallel architectures: single instruction single data (SISD), single instruction multiple data (SIMD), multiple instruction single data (MISD), and multiple instruction multiple data (MIMD). MIMD systems are the most common and can have either shared or distributed memory. Effective parallel programming requires approaches like message passing or shared memory models to facilitate communication between processing elements.
This document discusses general purpose computing on graphics processing units (GPUs) and solutions like NVIDIA CUDA and OpenCL. It then summarizes algorithms and operations that can be performed on GPUs using the Thrust library, including sorting, set operations, reductions, searching, and random number generation. Examples of applications that can be implemented with Thrust include bounding box computation, Voronoi diagrams, and Monte Carlo methods.
Slides for VU Web Technology course lecture on "Search on the Web". Explaining how search engines work, some basic information laws and inverted indices.
This document discusses parallel programming concepts including threads, synchronization, and barriers. It defines parallel programming as carrying out many calculations simultaneously. Advantages include increased computational power and speed up. Key issues in parallel programming are sharing resources between threads, and ensuring synchronization through locks and barriers. Data parallel programming is discussed where the same operation is performed on different data elements simultaneously.
詹剑锋:Big databench—benchmarking big data systemshdhappy001
This document discusses BigDataBench, an open source project for big data benchmarking. BigDataBench includes six real-world data sets and 19 workloads that cover common big data applications and preserve the four V's of big data. The workloads were chosen to represent typical application domains like search engines, social networks, and e-commerce. BigDataBench aims to provide a standardized benchmark for evaluating big data systems, architectures, and software stacks. It has been used in several case studies for workload characterization and evaluating the performance and energy efficiency of different hardware platforms for big data workloads.
The document discusses types of parallelism in hardware, software, and applications. It covers parallel architectures like multicore processors and clusters. Flynn's taxonomy classifies computers based on instruction and data streams as SISD, SIMD, MISD, and MIMD. Memory models include shared memory and message passing. Examples show parallelization of an equation solver kernel using instruction-level, task-level, and data-level parallelism. Speedup metrics like problem-constrained and time-constrained scaling are also introduced.
Intel's Nehalem Microarchitecture by Glenn Hintonparallellabs
Intel's Nehalem family of CPUs span from large multi-socket 32 core/64 thread systems to ultra small form factor laptops. What were some of the key tradeoffs in architecting and developing the Nehalem family of CPUs? What pipeline should it use? Should it optimize for servers? For desktops? For Laptops? There are lots of tradeoffs here. This talk will discuss some of the tradeoffs and results.
The document discusses different types of parallelism that can be utilized in parallel database systems: I/O parallelism to retrieve relations from multiple disks in parallel, interquery parallelism to run different queries simultaneously, intraquery parallelism to parallelize operations within a single query, and intraoperation parallelism to parallelize individual operations like sort and join. It also covers techniques for partitioning relations across disks and handling skew to balance the workload.
Run-Time Adaptive Processor Allocation of Self-Configurable Intel IXP2400 Net...CSCJournals
An ideal Network Processor, that is, a programmable multi-processor device must be capable of offering both the flexibility and speed required for packet processing. But current Network Processor systems generally fall short of the above benchmarks due to traffic fluctuations inherent in packet networks, and the resulting workload variation on individual pipeline stage over a period of time ultimately affects the overall performance of even an otherwise sound system. One potential solution would be to change the code running at these stages so as to adapt to the fluctuations; a near robust system with standing traffic fluctuations is the dynamic adaptive processor, reconfiguring the entire system, which we introduce and study to some extent in this paper. We achieve this by using a crucial decision making model, transferring the binary code to the processor through the SOAP protocol.
This document discusses parallel computing. It begins by defining parallel processing as using simultaneous data processing tasks to save time and/or money and solve larger problems. It then discusses how parallel computing uses multiple compute resources simultaneously to solve computational problems. Some examples of parallel phenomena in nature and technology are provided. The document outlines several areas where parallel computing is applied, including physics, bioscience, and computer science. It discusses the benefits of parallel computing in saving time and money and solving larger problems too large for a single computer. Finally, it briefly mentions ways to classify parallel computers and some basic requirements for achieving parallel execution.
This document discusses storage management in database systems. It describes the storage device hierarchy from fastest but smallest (cache) to slowest but largest (magnetic tapes). It covers main memory, hard disks, solid state drives and tertiary storage. The document also discusses RAID configurations and how the relational model is represented on secondary storage through records, blocks, files and indexes.
An asynchronous replication model to improve data available into a heterogene...Alexander Decker
This document summarizes a research paper that proposes an asynchronous replication model to improve data availability in heterogeneous systems. The proposed model uses a loosely coupled architecture between main and replication servers to reduce dependencies. It also supports heterogeneous systems, allowing different parts of an application to run on different systems for better performance. This makes it a cost-effective solution for data replication across different system types.
This document provides an overview of parallel and distributed systems. It discusses that a parallel computer contains multiple processing elements that communicate and cooperate to solve problems quickly, while a distributed system contains independent computers that appear as a single system. It notes that parallel computers are implicitly distributed systems. It then discusses reasons for using parallel and distributed computing like Moore's law and limitations of sequential processing due to power and latency walls. Finally, it outlines some topics that will be covered in the course like different parallel computing platforms, programming paradigms, and challenges in parallel and distributed systems.
1. File allocation methods include contiguous, linked, and indexed allocation. Contiguous allocation stores files in contiguous blocks but can lead to fragmentation. Linked allocation stores non-contiguous blocks through pointers but has overhead. Indexed allocation uses index blocks to point to data blocks.
2. File systems manage free space through data structures like bit vectors, linked lists, and space maps. Bit vectors require extra space but allow contiguous allocation. Linked lists have no wasted space but non-contiguous allocation. Space maps divide devices into metaslabs for efficient free space management.
3. Performance depends on allocation algorithms, metadata handling, buffer caching, and write policies. Techniques like read-ahead and free-behind optimize sequential access,
The document provides an overview of parallel processing and multiprocessor systems. It discusses Flynn's taxonomy, which classifies computers as SISD, SIMD, MISD, or MIMD based on whether they process single or multiple instructions and data in parallel. The goals of parallel processing are to reduce wall-clock time and solve larger problems. Multiprocessor topologies include uniform memory access (UMA) and non-uniform memory access (NUMA) architectures.
This document discusses various applications of parallel processing. It describes how parallel processing is used in numeric weather prediction to forecast weather by processing large amounts of observational data. It is also used in oceanography and astrophysics to study oceans and conduct particle simulations. Other applications mentioned include socioeconomic modeling, finite element analysis, artificial intelligence, seismic exploration, genetic engineering, weapon research, medical imaging, remote sensing, energy exploration, and more. The document also discusses loosely coupled and tightly coupled multiprocessors and the differences between the two approaches.
This document discusses using distributed processing to increase computational power. It introduces the concepts of using a distributed file system like HDFS to store large files across multiple machines. MapReduce is presented as a way to process large amounts of data in parallel by splitting it into chunks, mapping functions to each chunk, and then recombining the results. Hadoop is introduced as an open-source MapReduce framework that uses HDFS for storage and allows processing of data across a cluster of machines. Examples are given of writing Map and Reduce functions and running jobs on Hadoop to demonstrate distributed processing of large datasets.
The document compares the performance of two in-memory data platforms, Hazelcast and Infinispan, based on benchmarks of read and write operations using different numbers of load producing threads and server nodes. Key findings include that Hazelcast had faster average read and write times than Infinispan, lower response time deviations, and higher throughput rates, especially with 4 server nodes. Hazelcast also used system resources like CPU and memory more efficiently.
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
This document describes PowerAlluxio, an in-memory file system that improves on Alluxio by enabling shared memory utilization across cluster nodes while maintaining memory locality. PowerAlluxio allows client nodes to utilize remote node memory if local memory is full, improving cluster memory usage without sacrificing performance. It also introduces a new Smart LRU eviction policy that reduces elapsed time by 24.76% for large datasets. Experiments showed PowerAlluxio achieved up to 14.11x faster task completion times compared to Alluxio when data could be fully cached.
Distributed Framework for Data Mining As a Service on Private CloudIJERA Editor
Data mining research faces two great challenges: i. Automated mining ii. Mining of distributed data.
Conventional mining techniques are centralized and the data needs to be accumulated at central location. Mining
tool needs to be installed on the computer before performing data mining. Thus, extra time is incurred in
collecting the data. Mining is 4 done by specialized analysts who have access to mining tools. This technique is
not optimal when the data is distributed over the network. To perform data mining in distributed scenario, we
need to design a different framework to improve efficiency. Also, the size of accumulated data grows
exponentially with time and is difficult to mine using a single computer. Personal computers have limitations in
terms of computation capability and storage capacity.
Cloud computing can be exploited for compute-intensive and data intensive applications. Data mining
algorithms are both compute and data intensive, therefore cloud based tools can provide an infrastructure for
distributed data mining. This paper is intended to use cloud computing to support distributed data mining. We
propose a cloud based data mining model which provides the facility of mass data storage along with distributed
data mining facility. This paper provide a solution for distributed data mining on Hadoop framework using an
interface to run the algorithm on specified number of nodes without any user level configuration. Hadoop is
configured over private servers and clients can process their data through common framework from anywhere in
private network. Data to be mined can either be chosen from cloud data server or can be uploaded from private
computers on the network. It is observed that the framework is helpful in processing large size data in less time
as compared to single system.
Parallel processing architectures allow for simultaneous computation across multiple processing elements. There are four main types of parallel architectures: single instruction single data (SISD), single instruction multiple data (SIMD), multiple instruction single data (MISD), and multiple instruction multiple data (MIMD). MIMD systems are the most common and can have either shared or distributed memory. Effective parallel programming requires approaches like message passing or shared memory models to facilitate communication between processing elements.
This document discusses general purpose computing on graphics processing units (GPUs) and solutions like NVIDIA CUDA and OpenCL. It then summarizes algorithms and operations that can be performed on GPUs using the Thrust library, including sorting, set operations, reductions, searching, and random number generation. Examples of applications that can be implemented with Thrust include bounding box computation, Voronoi diagrams, and Monte Carlo methods.
Slides for VU Web Technology course lecture on "Search on the Web". Explaining how search engines work, some basic information laws and inverted indices.
The document provides information about inverted indexes and how they are used in web search systems. Some key points:
- An inverted index stores a list of documents that contain each word, allowing fast search for individual terms. It consists of a dictionary file and postings file.
- Web search engines build a central inverted index distributed across many computers to index the vast number of documents across the web.
- Web crawlers (spiders) recursively download pages starting from seed URLs to populate the index. Crawlers must handle challenges like duplicate pages, dynamic content, and being polite by not overloading websites.
An inverted file indexes a text collection to speed up searching. It contains a vocabulary of distinct words and occurrences lists with information on where each word appears. For each term in the vocabulary, it stores a list of pointers to occurrences called an inverted list. Coarser granularity indexes use less storage but require more processing, while word-level indexes enable proximity searches but use more space. The document describes how inverted files are structured and constructed from text and discusses techniques like block addressing that reduce their space requirements.
Discusses the concept of information seeking and 3 approaches to understanding it: Belkin's ASK hypothesis, Kuhlthau's Information Search Process and Dervin's Sense-Making.
Practical Elasticsearch - real world use casesItamar
Elasticsearch - a search and real-time analytics server based on Apache Lucene - is gaining a lot of popularity lately, and is being used world-wide to power many sophisticated systems. While many use it for the "standard" stuff (that is, simple full-text search and real-time log analysis), there are some really interesting usage patterns that can prove useful in many real-world scenarios. In this talk we will briefly talk about Elasticsearch and its common use-cases, and then showcase some less common use-cases leveraging Elasticsearch in an interesting and often times innovating ways.
Information searching & retrieving techniques khalidKhalid Mahmood
This document provides an overview of key concepts in information searching and retrieval, including definitions of information, information representation, information retrieval, databases, search mechanisms, browsing, language, interfaces, search strategies, and retrieval performance. It also describes common retrieval techniques like basic Boolean operators, phrase searching, truncation, proximity searching, focusing searches, fuzzy searching, weighted searching, query expansion, and searching multiple databases.
Elasticsearch Distributed search & analytics on BigData made easyItamar
Elasticsearch is a cloud-ready, super scalable search engine which is gaining a lot of popularity lately. It is mostly known for being extremely easy to setup and integrate with any technology stack.In this talk we will introduce Elasticdearch, and start by looking at some of its basic capabilities. We will demonstrate how it can be used for document search and even log analytics for DevOps and distributed debugging, and peek into more advanced usages like the real-time aggregations and percolation. Obviously, we will make sure to demonstrate how Elasticsearch can be scaled out easily to work on a distributed architecture and handle pretty much any load.
This document discusses information retrieval techniques. It begins by defining information retrieval as selecting the most relevant documents from a large collection based on a query. It then discusses some key aspects of information retrieval including document representation, indexing, query representation, and ranking models. The document also covers specific techniques used in information retrieval systems like parsing documents, tokenization, removing stop words, normalization, stemming, and lemmatization.
This document provides a summary of a typical day at Westside school. It is divided into 5 houses and offers clubs, sports, and places to study like the library. The day includes classes, lunch between 4th and 5th period, and buses waiting outside at the end to take students home. Breaking rules could result in in-school suspension.
ESPN Covers Rafael Nadal’s Next French Open Run Jed Drake
Jed Drake, senior vice president of ESPN, oversees ESPN's coverage of major international sporting events like the French Open. In 2014, ESPN2 and ESPN3 will continue broadcasting the French Open, a prestigious clay court tennis tournament. Rafael Nadal, a 27-year old Spanish tennis player, has dominated the French Open, winning a record 8 titles there and boasting an impressive 28-0 career record on the red clay of Roland Garros heading into the 2014 competition in Paris, France.
Pankaj has 6 years of experience in corporate training, leadership development, conflict management, and time management. He uses experiential and theoretical methods both indoors and outdoors. Some of his clients include IBM, Daksh, Convergys, Coca Cola, Agilent Technologies, AT&T, Serco and Teleperformance. Pankaj's training methodology focuses on creating a comfortable environment using humor and then questioning participants' beliefs to open them up to new perspectives in order to achieve goals.
CHRISTIAN SUPERNATURAL TEACHINGS, BIBLE CLASS LESSONS, GOSPELS BY LEADER OLUMBA OLUMBA OBU, THE SUPERNATURAL TEACHER AND SOLE SPIRITUAL HEAD, BROTHERHOOD OF THE CROSS AND STAR
This document describes the results of a survey conducted to understand preferences for features of a work-life balance app. 366 respondents participated in a conjoint analysis survey, rating their preferences for different app profiles that varied by operating system, customizability, and display resolution. The key findings are:
1) Customizability was found to be the most important feature, influencing 32% of preferences.
2) Operating systems like Windows and iOS were preferred over Android.
3) Higher display resolutions like 720p and 1080p were preferred over lower resolutions like 480p.
4) A regression equation was developed to predict preferences based on feature levels.
The document describes the results of a survey about life stages. The survey asked 10 multiple choice questions about topics like age of first kiss, appropriate age for marriage and children, and opinions on teenage driving and marriage. It then provides the percentage results for each multiple choice answer across the 10 questions, with most respondents falling into the expected mainstream choices for each issue.
The document discusses Microsoft data controls for accessing and displaying database information. It mentions the ADO Data Control and Data Grid control which allow connecting to and presenting data from databases. The document also states that it is the first time adding a database to a project.
SF Big Analytics & SF Machine Learning Meetup: Machine Learning at the Limit ...Chester Chen
Machine Learning at the Limit
John Canny, UC Berkeley
How fast can machine learning and graph algorithms be? In "roofline" design, every kernel is driven toward the limits imposed by CPU, memory, network etc. This can lead to dramatic improvements: BIDMach is a toolkit for machine learning that uses rooflined design and GPUs to achieve two- to three-orders of magnitude improvements over other toolkits on single machines. These speedups are larger than have been reported for *cluster* systems (e.g. Spark/MLLib, Powergraph) running on hundreds of nodes, and BIDMach with a GPU outperforms these systems for most common machine learning tasks. For algorithms (e.g. graph algorithms) which do require cluster computing, we have developed a rooflined network primitive called "Kylix". We can show that Kylix approaches the rooline limits for sparse Allreduce, and empirically holds the record for distributed Pagerank. Beyond rooflining, we believe there are great opportunities from deep algorithm/hardware codesign. Gibbs Sampling (GS) is a very general tool for inference, but is typically much slower than alternatives. SAME (State Augmentation for Marginal Estimation) is a variation of GS which was developed for marginal parameter estimation. We show that it has high parallelism, and a fast GPU implementation. Using SAME, we developed a GS implementation of Latent Dirichlet Allocation whose running time is 100x faster than other samplers, and within 3x of the fastest symbolic methods. We are extending this approach to general graphical models, an area where there is currently a void of (practically) fast tools. It seems at least plausible that a general-purpose solution based on these techniques can closely approach the performance of custom algorithms.
Bio
John Canny is a professor in computer science at UC Berkeley. He is an ACM dissertation award winner and a Packard Fellow. He is currently a Data Science Senior Fellow in Berkeley's new Institute for Data Science and holds a INRIA (France) International Chair. Since 2002, he has been developing and deploying large-scale behavioral modeling systems. He designed and protyped production systems for Overstock.com, Yahoo, Ebay, Quantcast and Microsoft. He currently works on several applications of data mining for human learning (MOOCs and early language learning), health and well-being, and applications in the sciences.
This document discusses the history of computer development and trends in computer hardware over time. It provides examples of early mainframe computers from 1965 that occupied entire rooms and cost millions, compared to modern laptops from 2008 that are thousands of times more powerful yet small and inexpensive. It outlines Moore's Law and trends related to transistor counts doubling every 1-2 years and processor performance doubling every 18 months. The document also discusses shrinking chip sizes over time and the limits of chip manufacturing.
This document discusses hardware provisioning best practices for MongoDB. It covers key concepts like bottlenecks, working sets, and replication vs sharding. It also presents two case studies where these concepts were applied: 1) For a Spanish bank storing logs, the working set was 4TB so they provisioned servers with at least that much RAM. 2) For an online retailer storing products, testing found the working set was 270GB, so they recommended a replica set with 384GB RAM per server to avoid complexity of sharding. The key lessons are to understand requirements, test with a proof of concept, measure resource usage, and expect that applications may become bottlenecks over time.
MySQL NDB Cluster 8.0 SQL faster than NoSQL Bernd Ocklin
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MongoDB has taken a clear lead in adoption among the new generation of databases, including the enormous variety of NoSQL offerings. A key reason for this lead has been a unique combination of agility and scalability. Agility provides business units with a quick start and flexibility to maintain development velocity, despite changing data and requirements. Scalability maintains that flexibility while providing fast, interactive performance as data volume and usage increase. We'll address the key organizational, operational, and engineering considerations to ensure that agility and scalability stay aligned at increasing scale, from small development instances to web-scale applications. We will also survey some key examples of highly-scaled customer applications of MongoDB.
This document discusses new data applications like machine learning and deep learning and their implications for storage. It notes that these applications deal with large and diverse data types including time series, matrices, and graphs. They have relaxed requirements for data correctness and persistence compared to traditional transactions. Opportunities exist to optimize storage for these workloads through techniques like tiering across memory types, streamlining data access, and exploiting lineage metadata to cache intermediate results. Fundamental shifts may also be possible by integrating analytics optimizations into storage management.
Factored Operating Systems (fos) - The Case for a Scalable Operating System for Multicores - Designing a new operating system targeting manycore
systems with scalability as the primary design constraint,
where space sharing replaces time sharing to increase
scalability.
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Seagate Kinetic Open Storage Platform provides a key-value storage interface that allows applications to directly access storage drives, bypassing file systems and other software layers. This dis-intermediation approach aims to lower total cost of ownership by reducing complexity and enabling more efficient use of hardware. The platform uses a distributed architecture with peer-to-peer data replication across drives to provide high performance, reliability and scalability. It also offers an open source software library and API to allow third party developers to build new storage applications and systems.
NYJavaSIG - Big Data Microservices w/ SpeedmentSpeedment, Inc.
Microservices solutions can provide fast access to large datasets by synchronizing SQL data into an in-JVM memory store and using key-value and column key stores. This allows querying terabytes of data in microseconds by mapping the data in memory and providing application programming interfaces. The solution uses periodic synchronization to initially load and periodically reload data, as well as reactive synchronization to capture and replay database changes.
This document describes a student's project to design an energy efficient cache memory using Verilog HDL. It presents the background and motivation, including that write-through caches consume a lot of energy due to increased access at lower levels. The proposed work introduces a way-tagged cache architecture to reduce energy consumption. Simulation results show energy savings of the proposed cache compared to a conventional cache for various operations like reset, data load, read and write hits/misses. The conclusion is that the way-tagged cache adds new components to L1 cache but remains inbuilt with the processor, with area overhead as a drawback.
1. In Memory Grids break problems into parts that can be solved using multiple resources on a network, using main memory instead of disk for faster file I/O.
2. In Memory Compute Grids allow computation tasks to be split and executed in parallel across grid nodes, while In Memory Data Grids provide applications with the ability to keep frequently accessed data in memory across multiple JVMs for high availability and low latency access.
3. Reference architectures show how In Memory Grids distribute data, computation tasks, and resources across a cluster for real-time processing of large datasets.
1. Building exascale computers requires moving to sub-nanometer scales and steering individual electrons to solve problems more efficiently.
2. Moving data is a major challenge, as moving data off-chip uses 200x more energy than computing with it on-chip.
3. Future computers should optimize for data movement at all levels, from system design to microarchitecture, to minimize energy usage.
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This document discusses scalable storage solutions for massive volumes of data. It introduces the concept of generalized deduplication as an extension of classic deduplication that can further reduce storage needs. Several research projects are described that utilize generalized deduplication, including MinervaFS, a file system, Alexandria, a cloud storage system, and Hermes, a data transfer protocol. MinervaFS was found to reduce storage usage for various datasets by up to 63.73% compared to other techniques like compression and classic deduplication. Alexandria demonstrated storage reductions of up to 14.49% in cloud storage configurations. Hermes aims to reduce data transmission costs through in-network deduplication.
The document discusses the history and evolution of computer hardware from the first generation of vacuum tube computers to current generation computers using grand-scale integrated circuits. It describes the main components of computer hardware including the central processing unit, primary and secondary storage, and input/output devices. It also covers topics such as computer memory, microprocessors, and emerging technologies.
How to approach a problem from a performance standpoint. A small real world application is used as a case study.
I\'ve presented "High Performance With Java" at Codebits\'2008 held from 13 to 15 November 2008
(*) Codebits is a programming contest held in Portugal held the spirit of Yahoo Hack! Day
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This document compares RISC and CISC architectures by examining the MIPS R2000 and Intel 80386 processors. It discusses the history of RISC and CISC, providing examples of each. Experiments using benchmarks show that while the 80386 executes fewer instructions on average than the R2000, the difference is small at around a 2x ratio. Both instruction sets are becoming more alike over time. In the end, performance depends more on how fast a chip executes rather than whether it is RISC or CISC.
Similar to What should be done to IR algorithms to meet current, and possible future, hardware trends. (20)
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Invited talk at the Workshop on Large-Scale and Distributed Systems for Information Retrieval 2015.
Cxense helps companies understand their audience and build great online experiences. Cxense Insight and DMP let customers annotate, filter, segment and target their users based on the consumed content and performed actions in real-time. With more than 5000 active websites, Insight alone tracks more than a billion unique users with more than 15 billions page views per month. To leverage the huge amounts of data in real-time, we have built a large distributed system relying on techniques familiar from databases, information retrieval and data mining. In this talk, we outline our solutions and give some insight into the technology we use and the challenges we face. This introduction should be interesting to undergraduate and PhD students as well as experienced researchers and engineers.
Abstract: Cxense Insight helps companies to understand their audience and build great online experiences. Our interactive UI and APIs help customers to
annotate, filter, segment and target their users based on the visited content and actions in realtime. Today we already track more than half a billion of unique user identities across more than 5000 web-sites, contributing to more than 10 billions of analytics events on a monthly basis.
To leverage these amounts of data in realtime, we built a large distributed system relying on the concepts familiar from databases, information retrieval and data mining. The first part of this talk will therefore give an insight into the challenges, the architecture and the techniques we have used. While the second part of the talk will briefly demonstrate our UI and APIs in action. We hope that both parts will be interesting for undergraduate students taking IR/DB courses as well as PhD students, experienced researchers and staff.
Spark is a framework for efficient parallel data processing. It uses resilient distributed datasets (RDDs) that can be operated on in parallel, cached in memory, and recomputed when needed. The core of Spark provides functions for data sharing and basic operations like filtering, mapping, and reducing RDDs. Additional Spark modules provide capabilities for SQL, streaming, machine learning, and graph processing.
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2) Skipping and pruning techniques like efficient compression and linear programming to improve pruning for disjunctive queries.
3) Caching approaches including modeling static two-level caching and prefetching query results to improve search engine performance. The research is evaluated using large test collections and clusters of up to 9 nodes.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
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GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
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My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
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11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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- The top challenges for privacy leaders, practitioners, and organizations in 2024
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Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
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What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
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In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Programming Foundation Models with DSPy - Meetup Slides
What should be done to IR algorithms to meet current, and possible future, hardware trends.
1. Hardware Developments and Algorithm Design:
“What should be done to IR algorithms to meet
current, and possible future, hardware trends?”
Simon Jonassen
Department of Computer and Information Science
Norwegian University of Science and Technology
2. This talk is not about….
Uncovered, but highly related topics:
– Query processing on specialized hardware, including GPU.
– Succinct indexes, suffix arrays, wavelet trees, etc.
– Map-Reduce and machine learning.
– Green and Cloud computing.
– Distributed query processing.
– Shared memory and NUMA.
– Scalability and availability.
– Solid-state drives.
– Virtualization.
– …
3. Information Retrieval
Information Retrieval (IR): representing, searching and manipulating large collections of
electronic and human-language data.
Scope for this talk:
• Indexed search in document collections.
Other examples and applications:
• Clustering and categorization.
• Information extraction.
• Question answering.
• Multimedia retrieval.
• Real-time search.
• Etc.
Index
Search Engine
Documents
Documents
Results
Queries
Users
5. Recent hardware trends
seen from a naïve IR perspective
Scope for
this talk.
4x512MB-
2GHz--
80GB-
4x2x3GHz++
4x8GB+
512GB
fast!
not so fast =( fast!
super fast!!!
2002 2012
DiskProcessor
Main
Memory
6. CPU: From GHz to multi-core
Moore’s Law:
• ~ the number of transistors on
an IC doubles every two years.
– Less space, more complexity.
– Shorter gates, higher clock rate.
Strategy of the 80s and 90’s:
• Add more complexity!
• Increase the clock rate!
Pollack’s Rule:
• The performance increase is ~
square root of the increased
complexity. [Borkar 2007]
The Power Wall:
• Increasing clock rate and transistor
current leakage lead to excess power
consumption, while RC delays in signal
transmission grow as feature sizes
shrink. [Borkar et al. 2005]
7. Instruction-level parallelism
– ”It’s not about GHz’s, but how you spend them!”
Pipeline length: 31 (P4) vs 14 stages (i7).
Multiple execution units and
out-of-order execution:
• i7: 2 load/store address, 1 store data,
and 3 computational operations can
be executed simultaneously.
Dependences and hazards:
• Control: branches.*
• Data: output dependence,
antidependence (naming).
• Structural: access to the same
physical unit of the processor.
Simultaneous multi-threading (“Hyper-threading”):
• Duplicate certain sections of a processor (registers etc., but not execution units).
• Reduces the impact of cache miss, branch misprediction and data dependency stalls.
• Drawback: logical processors are most likely to be treated just like physical processors.
(*[Dean 2010]: a branch misprediction costs ~5ns)
9. Computer memory hierarchy
L1-L3 cache and performance implications
Some of the main challenges of CMP:
• Cache coherence
• Cache conflicts
• Cache affinity
Other important cache-related issues:
• Data size and cache line utilization.
– i7 has 64B cache lines.
• Data alignment and padding.
• Cache associativity and replacement.
Additional memory issues:
• A large span of random memory accesses may
have additional slowdown due to TLB misses.
• Some of the virtual memory pages can also
be swaped out to disk.
Core
32KB
L1D
Core
32KB
L1D
256KB
L2
256KB
L2
8MB L3
Main memory
Thread1 Thread2
Core
32KB
L1D
Core
32KB
L1D
256KB
L2
256KB
L2
Thread3 Thread4
10. Writing efficient IR algorithms
–”The troubles with radix sort are in implementation, not in conception.” (McIlroy et al. 1993)
In-Place MSB Radix Sort:
[Birkeland 2008, Gorset 2011]
• Starting from the most significant byte.
• For each of the 256 combinations: count
the cardinality and Initialize the pointers.
• Apply Counting-Sort (shown on the right).
• Recursively apply on the less significant
byte until the least significant byte; use
insertion sort if the range is too small.
Complexity:
• O(kN), where k = 4 for 32-bit integers.
• Has also been shown to be 3x faster than the native Java/C++
QuickSort implementation on large 32-bit integer arrays [Gorset 2011].
Benefits from:
• Memory usage.
• Comparing groups of bits at once.
• Swaps instead of branches.
code: https://github.com/gorset/radix
11. Writing efficient IR algorithms
Cache- and processor-efficient query processing
Modern compression methods for IR:
• BP, S9/S16, PFOR, NewPFD, etc.
• Fast, superscalar and branch-free.
• Loops/methods can be generated by a script.
While compression works on chunks of
postings, query processing itself remains
posting-at-a-time.
What about:
• Branches and loops?
• Cache utilization?
• ILP utilization?
• Candidates and results?
Interesting alternatives and trade-offs:
• Impact-ordered vs document-ordered lists.
• Term vs document-at-a-time processing.
• Posting list iteration vs random access.
• Mixed vs two-phase search.
• Bitmaps vs posting lists.
code: https://github.com/javasoze/kamikaze
12. source: [Zukowski 2009]
Writing efficient IR algorithms
Some experience from Databases
Vector-at-a-time execution [Zukowski 2009]
provides a good trade-off between tuple and
column-at-a-time execution:
• Less time spent in interpretation logic.
• “SIMDization” and data alignment.
• Parallel memory access (prefetching).
• In-cache execution.
Loop compilation can be another
alternative, especially if the application
already has a tuple-at-a-time API.
• [Sompolski et al. 2011] show that
plain loop compilation can be inferior
to vectorization and motivate further
combination of the two techniques.
13. Concurrent query processing
– In-memory indexes and “1024 core CPU”s: What to expect?
Inter-query vs intra-query concurrency:
• Inter:
– Each thread works with a different query.
– Improves throughput, but latency may degrade.
• Intra:
– A query is processed by multiple threads.
– Improves latency, but throughput may degrade.
Inter-query concurrency and memory access:
• [Strohman and Croft 2007]:
– Top-k query processing with impact-ordered lists.
– Observed that shared memory bandwidth
becomes a bottleneck with four processors.
• [Tatikonda et al. 2009]:
– Intersection with document-ordered lists.
– Observed no cache or memory bandwidth problems.
• [Qiao et al. 2008]:
– DBMS query processing with a very large table.
– Demonstrated that when all cores are used,
main memory bandwidth becomes bottleneck.
source: [Qiao et al. 2008]
14. Concurrent query processing
– In-memory indexes and “1024 core CPU”s: What to expect?
Intra-query concurrency and memory access:
• [Lilleengen 2010]:
– CPU simulator for Vespa Search Engine Platform (Yahoo! Trondheim).
– Evaluated intra-query concurrency, its scalability, impact on the
processor caches and performance under various workloads.
Other ideas:
• [Qiao et al. 2008] studied efficient memory scan sharing
for multi-core CPUs in databases. Suggested solution:
– Each core gets a batch of queries, restricted by the
estimated working set size.
– Queries in each batch share memory scans, i.e., a
block of data is fed to through all queries in the batch.
– Note: queries operate on a single but very large table.
• Batch optimizations similar to those presented by
[Ding et al. 2011] can be interesting on sub-query level.
– Query reordering.
– Reusing partial results.
source: [Qiao et al. 2008]
18. One more thing… Java!
Bytecode and Just-in-time (JIT) compilation:
• Java bytecode is halfway between the human-readable and machine code.
• Bytecode can be interpreted by JVM or compiled to machine code at runtime.
• JIT/HotSpot tricks: inlining, dead-code elimination, optimization/deoptimization.
• Intrinsics: some functions can be replaced by machine instructions (e.g., popcount, max/min).
Concurrent processing in Java:
• Powerful and flexible features (e.g., thread pools,
synchronous data structures, Fork/Join).
• To be efficient, needs a careful understanding of
synchronization and the Java memory model.
• Does not provide any affinity or low-level thread control.
Garbage collection (GC) and memory management:
• Multiple areas/generations: eden and survivor (young),
tenured (old), permgen (internal).
• Minor (young generation) vs major (old generation) GC.
• Low-pause vs high-throughput GC algorithms.
• Escape analysis.
19. One more thing… Java!
Efficiency tips:
• Data:
– Avoid big class hierarchies. Write simple and when applicable immutable objects.
– Avoid creating unnecessary objects, use primitives.
– Avoid frequent allocation of very large arrays.
• Methods:
– Write compact, clean, reusable and when applicable static methods.
• Concurrency:
– Divide and conquer!
– Minimize synchronization and resource sharing between threads.
• Development:
– Correctness over performance.
– Use existing collections and libraries.
– Learn to profile, version control and unit-test your code.
20. Conclusions
• Processors are getting faster and more advanced. However, these improvements are
becoming more challenging to harness by memory-intensive applications, such as IR.
• Future IR algorithms should pay more attention to the CPU and cache-related issues.
• Understanding of the hardware and programming language principles and their
interaction is essential for realization of conception advantage in performance of an
actual implementation.
• Certain optimizations and performance improvements can be limited to the chosen
architecture and/or technology. For large-scale and heterogeneous IR systems such
optimizations may be less beneficial, economically infeasible or even impossible.
• Low-power RISC processors are capable of delivering higher performance-per-watt
as well as performance-per-$ when compared to the high-end/desktop processors.
However, it remains unclear whether they can be more advantageous for efficient IR
and which challenges they may introduce.
21. References:
1. Birkeland: “Searching large data volumes with MISD processing”, PhD Thesis, NTNU 2008.
2. Borkar: "Thousand core chips: a technology perspective”, In Proc. DAC 2007, pp.746-749.
3. Borkar et al.: “Platform 2015: Intel® Processor and Platform Evolution for the Next Decade”, Intel 2005.
4. Bosworth: “The Power Wall: Why aren’t modern CPUs faster? What happened in the late 1990’s?”, 2011.
5. Büttcher et al.: “Information Retrieval: Implementing and Evaluating Search Engines”, 2010.
6. Chhugani et al.: “Efficient Implementation of Sorting on Multi-Core SIMD CPU Architecture”, In Proc. VLDB 2008, pp.
1313-1324.
7. Clark: “Facebook stretches ARM chips in datacentre tests”, ZDNet news article, 24th September 2012.
8. Dean: “Challenges in Building Large-Scale Information Retrieval Systems”, keynote at WSDM 2009.
9. Dean: “Building Software Systems at Google and Lessons Learned”, talk at Standford University 2010.
10. Ding et al.: “Batch Query Processing for Web Search Engines”, In Proc. WSDM 2011, pp. 137-146.
11. Evans and Verburg: “Well Grounded Java Developer: Vital Techniques of Java 7 and polyglot programming”, 2013.
12. Gorset: http://erik.gorset.no/2011/04/radix-sort-is-faster-than-quicksort.html, 2010.
13. Hennessy and Patterson: “Computer Architecture: A Quantitative Approach”, 3rd ed., 2003.
14. Jahre: “Managing Shared Resources in Chip Multiprocessor Memory Systems”, PhD Thesis, NTNU 2010.
15. Katsov: http://scalable.wordpress.com/2012/06/05/fast-intersection-sorted-lists-sse/, 2012.
16. Ladra et al.: “Exploiting SIMD instructions in current processors to improve classical string algorithms”, In Proc. ADBIS 2012,
pp. 254-267.
17. Lemire and Boytsov: “Decoding billions of integers per second through vectorization”, CoRR abs/1209.2137, 2012.
18. Lilleengen: “Parallel query evaluation on multicore architectures”, Master Thesis, NTNU 2010.
19. Qiao et al.: “Main-Memory Scan Sharing For Multi-Core CPUs”, PVLDB 2008:1(1), pp. 610-621.
20. Schlegel et al.: “Fast Sorted-Set Intersection using SIMD Instructions”, In ADMS Workshop, VLDB 2011.
21. Strohman and Croft: “Efficient Document Retrieval in Main Memory”, In Proc. SIGIR 2007, pp. 175-182.
22. Tatikonda et al.: “On efficient posting list intersection with multicore processors”, In Proc. SIGIR 2009, pp. 738-739.
23. Vasudevan et al.: “Challenges and Opportunities for Efficient Computing with FAWN”, In Proc. SIGOPS 2011, pp. 34-44.
24. Zukowski: “Balancing Vectorized Query Execution with Bandwidth-Optimized Storage”, PhD Thesis, University of
Amsterdam 2009.
25. Solid-State Drives
Based on NAND floating gate transistors.
Each disk is a redundant array of NAND.
Cannot delete/overwrite individual pages.
• Consequence: frequent writes are
problematic and write performance
degrades with aging.
• Solutions: 128MB+ on-board memory,
background garbage collection, trimming,
overprovisioning.
• Other (SandForce DuraWhite): compression, deduplication and differencing.
Lifetime is limited due to writes, but modern SSD should last as long as HDD.
Single-level vs multi-level charge:
• SLC is more reliable, but expensive
• MLC may have larger capacity/cheaper, but is less reliable.
26. Solid-State vs Hard-Disk Drives
SSD was found to improve the performance of
several applications such as spatial query
processing with R-trees ([Emrich et al. 2010]).
January 2013: A 3TB HDD and 32GB DRAM
cost less than a 512GB SSD.
SSD may be considered as infeasible for
large data centers
• see the discussion in the paper by
[Ananthanarayanan et a. 2011].
SSD and HDD can be combined in the
same system. (eg., [Risvik 2013])
SSD and HDD require different trade-offs.
Some
other
numbers
[Dean
2010]:
Send
2KB
over
1
Gbps
network
20µs
Round
trip
within
same
datacenter
500µs
Send
packet
CA-‐>Netherlands-‐>CA
150ms
Access
Dme
Bandwidth
Price
Capacity
HDD
3-‐12ms
<140MB/s
<0.05$/GB
1TB+
SSD
<100µs
<600MB/s
0.5-‐1$/GB
512GB-‐
DRAM
<50ns
<21GB/s
5-‐10$/GB
32GB-‐
27. References:
1. Ananthanarayanan et al.; “Disk-Locality in Datacenter Computing Considered Irrelevant”, In Proc. HotOS Workshop at
USENIX 2011.
2. Chhugani et al.: “Efficient Implementation of Sorting on Multi-Core SIMD CPU Architecture”, In Proc. VLDB 2008, pp.
1313-1324.
3. Emrich et al.: “On the Impact of Flash SSDs on Spatial Indexing”, In Proc. DaMoN 2010, pp. 3-8.
4. Hovland: “Throughput Computing on Future GPUs”, Master Thesis, NTNU 2009.
5. Hutchinson: “Solid-state revolution: in-depth on how SSDs really work”, ARS Technica, 2012.
6. Risvik et al.: “Maguro, a system for indexing and searching over very large text collections”, In Proc. WSDM 2013, To
appear.