Cluster Drm
•
0 likes•380 views
This document provides information about various Linux clusters and distributed resource managers available at the Center for Genome Science and NIH. It lists several Linux cluster machines including KHAN with 94 nodes, KGENE with 28 nodes, and LOGIN and LOGINDB servers. Details are provided about the KHAN cluster including its node configuration, storage space, and available software. It also briefly mentions distributed resource managers.
Report
Share
Report
Share
Download to read offline
Recommended
Curcumin job file
This script runs a Gaussian 09 quantum chemistry calculation using 2 CPUs on a single compute node. It sets up the environment, runs the calculation using the methane2.input file, and cleans up scratch files when finished. The calculation is small and only uses one fourth of the node's resources.
Annette g09 job file for cyclohexene
This script runs a Gaussian 09 computational chemistry job using 2 CPUs on a single compute node. It sets up the environment, runs the methane2.input input file in Gaussian 09 using the allocated resources of 2 CPUs and a portion of the node's memory and disk space, then cleans up the scratch directory.
Annette g09 job file for cyclohexene for niobium
This script runs a Gaussian 09 computational chemistry job using 2 CPUs on a single compute node, utilizing one fourth of the node's resources. It sets up the environment, runs the Niobium2.input job, and cleans up scratch files when finished.
Formaldehye2 job program
This script runs a Gaussian 09 quantum chemistry calculation using 2 CPUs on a single compute node. It sets up the environment and scratch directory for Gaussian, runs the formaldehyde2.input calculation, and cleans up files after completion. The calculation is configured to only use one quarter of an 8-core node's resources as a test, even though the methane problem is too small to require that much.
hbaseconasia2019 Spatio temporal Data Management based on Ali-HBase Ganos and...
Fei Xiao of Alibaba
Track 2: Ecology and Solutions
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
Upgrading To The New Map Reduce API
The document summarizes the key changes between the old MapReduce API and the new MapReduce API in Hadoop. Some of the main changes include:
- Renaming all "mapred" packages to "mapreduce"
- Methods can now throw InterruptedException in addition to IOException
- Using Configuration instead of JobConf
- Changes to Mapper, Reducer, and RecordReader interfaces and classes
- Submitting jobs uses the Job class instead of JobConf and JobClient classes
- InputFormats and OutputFormats see changes like new methods and removed classes
scalable machine learning
This document discusses scalable machine learning techniques. It summarizes Spark MLlib, which provides machine learning algorithms that can run on large datasets in a distributed manner using Apache Spark. It also discusses H2O, which provides fast machine learning algorithms that can integrate with Spark via Sparkling Water to allow transparent use of H2O models and algorithms with the Spark API. Examples of using K-means clustering and logistic regression are provided to illustrate MLlib and H2O.
On heap cache vs off-heap cache
The document compares on-heap and off-heap caching options. It discusses heap memory usage in the JVM and alternatives like off-heap memory using memory mapped files, ByteBuffers, and Unsafe. Popular off-heap caches like Chronicle, Hazelcast, and Redis are presented along with comparisons of their features, performance, and garbage collection impact. The document aims to help developers choose the most suitable cache for their application needs.
Recommended
Curcumin job file
This script runs a Gaussian 09 quantum chemistry calculation using 2 CPUs on a single compute node. It sets up the environment, runs the calculation using the methane2.input file, and cleans up scratch files when finished. The calculation is small and only uses one fourth of the node's resources.
Annette g09 job file for cyclohexene
This script runs a Gaussian 09 computational chemistry job using 2 CPUs on a single compute node. It sets up the environment, runs the methane2.input input file in Gaussian 09 using the allocated resources of 2 CPUs and a portion of the node's memory and disk space, then cleans up the scratch directory.
Annette g09 job file for cyclohexene for niobium
This script runs a Gaussian 09 computational chemistry job using 2 CPUs on a single compute node, utilizing one fourth of the node's resources. It sets up the environment, runs the Niobium2.input job, and cleans up scratch files when finished.
Formaldehye2 job program
This script runs a Gaussian 09 quantum chemistry calculation using 2 CPUs on a single compute node. It sets up the environment and scratch directory for Gaussian, runs the formaldehyde2.input calculation, and cleans up files after completion. The calculation is configured to only use one quarter of an 8-core node's resources as a test, even though the methane problem is too small to require that much.
hbaseconasia2019 Spatio temporal Data Management based on Ali-HBase Ganos and...
Fei Xiao of Alibaba
Track 2: Ecology and Solutions
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
Upgrading To The New Map Reduce API
The document summarizes the key changes between the old MapReduce API and the new MapReduce API in Hadoop. Some of the main changes include:
- Renaming all "mapred" packages to "mapreduce"
- Methods can now throw InterruptedException in addition to IOException
- Using Configuration instead of JobConf
- Changes to Mapper, Reducer, and RecordReader interfaces and classes
- Submitting jobs uses the Job class instead of JobConf and JobClient classes
- InputFormats and OutputFormats see changes like new methods and removed classes
scalable machine learning
This document discusses scalable machine learning techniques. It summarizes Spark MLlib, which provides machine learning algorithms that can run on large datasets in a distributed manner using Apache Spark. It also discusses H2O, which provides fast machine learning algorithms that can integrate with Spark via Sparkling Water to allow transparent use of H2O models and algorithms with the Spark API. Examples of using K-means clustering and logistic regression are provided to illustrate MLlib and H2O.
On heap cache vs off-heap cache
The document compares on-heap and off-heap caching options. It discusses heap memory usage in the JVM and alternatives like off-heap memory using memory mapped files, ByteBuffers, and Unsafe. Popular off-heap caches like Chronicle, Hazelcast, and Redis are presented along with comparisons of their features, performance, and garbage collection impact. The document aims to help developers choose the most suitable cache for their application needs.
HBaseCon 2013: OpenTSDB at Box
This document discusses Box's use of OpenTSDB to store and query time series metrics data. It describes how OpenTSDB provides a scalable and easy way to collect, store, and query large amounts of metrics data compared to previous solutions. It includes examples of using OpenTSDB, such as a script to collect MySQL metrics and adding it as a cron job, and examples of querying the data through the OpenTSDB API and web interface. It also provides some statistics about Box's OpenTSDB deployment and next steps.
HadoopThe Hadoop Java Software Framework
Storage and computation is getting cheaper AND easily accessible on demand in the cloud. We now collect and store some really large data sets Eg: user activity logs, genome sequencing, sensory data etc. Hadoop and the ecosystem of projects built around it present simple and easy to use tools for storing and analyzing such large data collections on commodity hardware.
Topics Covered
* The Hadoop architecture.
* Thinking in MapReduce.
* Run some sample MapReduce Jobs (using Hadoop Streaming).
* Introduce PigLatin, a easy to use data processing language.
Speaker Profile: Mahesh Reddy is an Entrepreneur, chasing dreams. Works on large scale crawl and extraction of structured data from the web. He is a graduate frm IIT Kanpur(2000-05) and previously worked at Yahoo! Labs as Research Engineer/Tech Lead on Search and Advertising products.
Gnocchi v4 - past and present
a dive into performance enhancements in Gnocchi v4.x (as of end of August) and how it compares to previous releases of Gnocchi.
Whirlwind tour of Hadoop and HIve
This document provides a whirlwind tour of Hadoop, describing its core components and how they work together. It discusses HDFS for distributed storage, MapReduce for distributed processing, and Hive for SQL-like queries on Hadoop datasets.
R user-group-2011-09
Talk given on September 21 to the Bay Area R User Group. The talk walks a stochastic project SVD algrorithm through the steps from initial implementation in R to a proposed implementation using map-reduce that integrates cleanly with R via NFS export of the distributed file system. Not surprisingly, this algorithm is essentially the same as the one used by Mahout.
Introduction to Hadoop Ecosystem
Did you like it? Check out our blog to stay up to date: https://getindata.com/blog
The presentation contents a fundamental knowlege about Hadoop Ecosystem. It includes a popular technology as HDFS, YARN, HIVE Spark and Flink
Big Data Analytics with Scala at SCALA.IO 2013
This document provides an overview of big data analytics with Scala, including common frameworks and techniques. It discusses Lambda architecture, MapReduce, word counting examples, Scalding for batch and streaming jobs, Apache Storm, Trident, SummingBird for unified batch and streaming, and Apache Spark for fast cluster computing with resilient distributed datasets. It also covers clustering with Mahout, streaming word counting, and analytics platforms that combine batch and stream processing.
Alexander Ignatyev "MapReduce infrastructure"
This document summarizes Yandex's MapReduce implementation. It describes Yandex's MapReduce clusters which contain 5 clusters with 2000 hosts and store 10PB of data. It also describes the structure of MapReduce tables which are divided into chunks and records. The document outlines some of Yandex's MapReduce applications like mr_cat, mr_cp and mr_grep which function similar to UNIX tools. It provides an overview of the Map and Reduce operations in Yandex MapReduce.
Gnocchi v3
The document summarizes optimisation opportunities and testing results for Gnocchi v3 compared to v2. It discusses improvements to coordination and scheduling to minimize contention, performance improvements for large datasets, a new storage format to reduce operations and disk size, and benchmark results showing processing time reductions of up to 23% and write throughput increases.
14 lab-planing
This document provides guidelines for planning the layout of services in a MapR HA cluster. It recommends:
- Running the TaskTracker and MapR-FS on most worker nodes
- Running control services like the CLDB, ZooKeeper, JobTracker on a few nodes
- Avoiding running ZooKeeper and CLDB on the same node, especially for clusters over 100 nodes
- Maintaining additional machines for failover purposes
It includes an example service layout and a table with recommended instances of services like CLDB, HBase, JobTracker, ZooKeeper, and others to use when designing a HA cluster service layout.
HBase Secondary Indexing
This document discusses secondary indexing in HBase. It introduces HBase and how scans work. Secondary indexes are created to avoid full table scans by storing row keys corresponding to indexed columns in a separate table. CoProcessors can be used to implement secondary indexes by assigning index regions and keeping them in sync with data regions. Benchmark results show secondary indexing improves query performance at the cost of extra storage and processing overhead from CoProcessors. Challenges include indexing across regions and handling region splits.
Introduction to Hadoop - FinistJug
This document introduces Hadoop, an open-source software platform for distributed storage and processing of large datasets across clusters of computers. It discusses the key components of Hadoop including the Hadoop Distributed File System (HDFS) for storage, MapReduce for processing, and YARN for resource management. The document also briefly introduces related projects like Pig for data flows and Hive for SQL-like queries that build on Hadoop.
Aws Quick Dirty Hadoop Mapreduce Ec2 S3
This document discusses using Amazon Web Services (AWS) and Hadoop MapReduce for quick and dirty parallel processing on the cloud. It provides an overview of AWS tools like EC2, S3, and Elastic MapReduce that can be used to launch and manage Hadoop clusters in the cloud. It also summarizes how MapReduce works and gives an example of analyzing Wikipedia page view logs with Hadoop Streaming and S3 for storage. The document concludes that Hadoop MapReduce provides an easy way to do distributed computing in the cloud.
Opportunities of ML-based data analytics in ABCI
This document discusses opportunities for using machine learning-based data analytics on the ABCI supercomputer system. It summarizes:
1) An introduction to the ABCI system and how it is being used for AI research.
2) How sensor data from the ABCI system and job logs could be analyzed using machine learning to optimize data center operation and improve resource utilization and scheduling.
3) Two potential use cases - using workload prediction to enable more efficient cooling system control, and applying machine learning to better predict job execution times to improve scheduling.
Hydra
Hydra is a Hadoop-style distributed processing framework optimized for building and navigating tree data structures. It includes components for job control, task running, querying, and a distributed filesystem. To get started, users install prerequisites like RabbitMQ and Maven, clone and build the Hydra repository, start the local stack, seed sample data, and can then run sample jobs and queries to see results. The document provides tips for analyzing text files with Hydra and concludes that it is well suited for applications that involve working with tree data structures.
Analytics at Speed: Introduction to ClickHouse and Common Use Cases. By Mikha...
ClickHouse is a powerful open source analytics database that provides fast, scalable performance for data warehousing and real-time analytics use cases. It can handle petabytes of data and queries and scales linearly on commodity hardware. ClickHouse is faster than other databases for analytical workloads due to its columnar data storage and parallel processing. It supports SQL and integrates with various data sources. ClickHouse can run on-premises, in the cloud, or in containers. The ClickHouse operator makes it easy to deploy and manage ClickHouse clusters on Kubernetes.
ABCI: An Open Innovation Platform for Advancing AI Research and Deployment
AI Infrastructure for Everyone (Democratization AI) aims to build an AI infrastructure platform that is accessible to everyone from beginners to experts. The platform provides up to 512-node computing resources, ready-to-use software, datasets, and pre-trained models. It also offers services like an easy-to-use web-based IDE for beginners and an AI cloud with on-demand, reserved, and batch processing options. The goal is to accelerate AI research and promote social implementation of AI technologies.
Hadoop 2
This document provides a technical introduction to Hadoop, including:
- Hadoop has been tested on a 4000 node cluster with 32,000 cores and 16 petabytes of storage.
- Key Hadoop concepts are explained, including jobs, tasks, task attempts, mappers, reducers, and the JobTracker and TaskTrackers.
- The process of launching a MapReduce job is described, from the client submitting the job to the JobTracker distributing tasks to TaskTrackers and running the user-defined mapper and reducer classes.
20181116 Massive Log Processing using I/O optimized PostgreSQL
The document describes a technology called PG-Strom that uses GPU acceleration to optimize I/O performance for PostgreSQL. PG-Strom allows data to be transferred directly from NVMe SSDs to the GPU over the PCIe bus, bypassing the CPU and RAM. This reduces data movement and allows PostgreSQL queries to be partially executed directly on the GPU. Benchmark results show the approach can achieve throughput close to the theoretical hardware limits for a single server configuration processing large datasets.
The Galaxy toolshed
This presentation provides some technical details on the function of the Galaxy toolshed. It was prepared for a group (Biobix at UGent), during my previous job.
Cell Biology Lecture #2
This document provides an overview of advanced cell biology topics covered in a 2014 semester course, including introduction, research strategies, organelles, membranes, trafficking, signaling, cytoskeleton, and cell cycle. It also discusses methods for studying cells, such as determining component lists through expression profiling, proteomics, localization techniques like immunofluorescence, and perturbation methods like RNAi and CRISPR/Cas9 knockouts. Microscopy techniques like fluorescence, confocal, and live cell imaging are explained. The document concludes with an overview of biochemical characterization and protein structure determination using X-ray crystallography.
More Related Content
What's hot
HBaseCon 2013: OpenTSDB at Box
This document discusses Box's use of OpenTSDB to store and query time series metrics data. It describes how OpenTSDB provides a scalable and easy way to collect, store, and query large amounts of metrics data compared to previous solutions. It includes examples of using OpenTSDB, such as a script to collect MySQL metrics and adding it as a cron job, and examples of querying the data through the OpenTSDB API and web interface. It also provides some statistics about Box's OpenTSDB deployment and next steps.
HadoopThe Hadoop Java Software Framework
Storage and computation is getting cheaper AND easily accessible on demand in the cloud. We now collect and store some really large data sets Eg: user activity logs, genome sequencing, sensory data etc. Hadoop and the ecosystem of projects built around it present simple and easy to use tools for storing and analyzing such large data collections on commodity hardware.
Topics Covered
* The Hadoop architecture.
* Thinking in MapReduce.
* Run some sample MapReduce Jobs (using Hadoop Streaming).
* Introduce PigLatin, a easy to use data processing language.
Speaker Profile: Mahesh Reddy is an Entrepreneur, chasing dreams. Works on large scale crawl and extraction of structured data from the web. He is a graduate frm IIT Kanpur(2000-05) and previously worked at Yahoo! Labs as Research Engineer/Tech Lead on Search and Advertising products.
Gnocchi v4 - past and present
a dive into performance enhancements in Gnocchi v4.x (as of end of August) and how it compares to previous releases of Gnocchi.
Whirlwind tour of Hadoop and HIve
This document provides a whirlwind tour of Hadoop, describing its core components and how they work together. It discusses HDFS for distributed storage, MapReduce for distributed processing, and Hive for SQL-like queries on Hadoop datasets.
R user-group-2011-09
Talk given on September 21 to the Bay Area R User Group. The talk walks a stochastic project SVD algrorithm through the steps from initial implementation in R to a proposed implementation using map-reduce that integrates cleanly with R via NFS export of the distributed file system. Not surprisingly, this algorithm is essentially the same as the one used by Mahout.
Introduction to Hadoop Ecosystem
Did you like it? Check out our blog to stay up to date: https://getindata.com/blog
The presentation contents a fundamental knowlege about Hadoop Ecosystem. It includes a popular technology as HDFS, YARN, HIVE Spark and Flink
Big Data Analytics with Scala at SCALA.IO 2013
This document provides an overview of big data analytics with Scala, including common frameworks and techniques. It discusses Lambda architecture, MapReduce, word counting examples, Scalding for batch and streaming jobs, Apache Storm, Trident, SummingBird for unified batch and streaming, and Apache Spark for fast cluster computing with resilient distributed datasets. It also covers clustering with Mahout, streaming word counting, and analytics platforms that combine batch and stream processing.
Alexander Ignatyev "MapReduce infrastructure"
This document summarizes Yandex's MapReduce implementation. It describes Yandex's MapReduce clusters which contain 5 clusters with 2000 hosts and store 10PB of data. It also describes the structure of MapReduce tables which are divided into chunks and records. The document outlines some of Yandex's MapReduce applications like mr_cat, mr_cp and mr_grep which function similar to UNIX tools. It provides an overview of the Map and Reduce operations in Yandex MapReduce.
Gnocchi v3
The document summarizes optimisation opportunities and testing results for Gnocchi v3 compared to v2. It discusses improvements to coordination and scheduling to minimize contention, performance improvements for large datasets, a new storage format to reduce operations and disk size, and benchmark results showing processing time reductions of up to 23% and write throughput increases.
14 lab-planing
This document provides guidelines for planning the layout of services in a MapR HA cluster. It recommends:
- Running the TaskTracker and MapR-FS on most worker nodes
- Running control services like the CLDB, ZooKeeper, JobTracker on a few nodes
- Avoiding running ZooKeeper and CLDB on the same node, especially for clusters over 100 nodes
- Maintaining additional machines for failover purposes
It includes an example service layout and a table with recommended instances of services like CLDB, HBase, JobTracker, ZooKeeper, and others to use when designing a HA cluster service layout.
HBase Secondary Indexing
This document discusses secondary indexing in HBase. It introduces HBase and how scans work. Secondary indexes are created to avoid full table scans by storing row keys corresponding to indexed columns in a separate table. CoProcessors can be used to implement secondary indexes by assigning index regions and keeping them in sync with data regions. Benchmark results show secondary indexing improves query performance at the cost of extra storage and processing overhead from CoProcessors. Challenges include indexing across regions and handling region splits.
Introduction to Hadoop - FinistJug
This document introduces Hadoop, an open-source software platform for distributed storage and processing of large datasets across clusters of computers. It discusses the key components of Hadoop including the Hadoop Distributed File System (HDFS) for storage, MapReduce for processing, and YARN for resource management. The document also briefly introduces related projects like Pig for data flows and Hive for SQL-like queries that build on Hadoop.
Aws Quick Dirty Hadoop Mapreduce Ec2 S3
This document discusses using Amazon Web Services (AWS) and Hadoop MapReduce for quick and dirty parallel processing on the cloud. It provides an overview of AWS tools like EC2, S3, and Elastic MapReduce that can be used to launch and manage Hadoop clusters in the cloud. It also summarizes how MapReduce works and gives an example of analyzing Wikipedia page view logs with Hadoop Streaming and S3 for storage. The document concludes that Hadoop MapReduce provides an easy way to do distributed computing in the cloud.
Opportunities of ML-based data analytics in ABCI
This document discusses opportunities for using machine learning-based data analytics on the ABCI supercomputer system. It summarizes:
1) An introduction to the ABCI system and how it is being used for AI research.
2) How sensor data from the ABCI system and job logs could be analyzed using machine learning to optimize data center operation and improve resource utilization and scheduling.
3) Two potential use cases - using workload prediction to enable more efficient cooling system control, and applying machine learning to better predict job execution times to improve scheduling.
Hydra
Hydra is a Hadoop-style distributed processing framework optimized for building and navigating tree data structures. It includes components for job control, task running, querying, and a distributed filesystem. To get started, users install prerequisites like RabbitMQ and Maven, clone and build the Hydra repository, start the local stack, seed sample data, and can then run sample jobs and queries to see results. The document provides tips for analyzing text files with Hydra and concludes that it is well suited for applications that involve working with tree data structures.
Analytics at Speed: Introduction to ClickHouse and Common Use Cases. By Mikha...
ClickHouse is a powerful open source analytics database that provides fast, scalable performance for data warehousing and real-time analytics use cases. It can handle petabytes of data and queries and scales linearly on commodity hardware. ClickHouse is faster than other databases for analytical workloads due to its columnar data storage and parallel processing. It supports SQL and integrates with various data sources. ClickHouse can run on-premises, in the cloud, or in containers. The ClickHouse operator makes it easy to deploy and manage ClickHouse clusters on Kubernetes.
ABCI: An Open Innovation Platform for Advancing AI Research and Deployment
AI Infrastructure for Everyone (Democratization AI) aims to build an AI infrastructure platform that is accessible to everyone from beginners to experts. The platform provides up to 512-node computing resources, ready-to-use software, datasets, and pre-trained models. It also offers services like an easy-to-use web-based IDE for beginners and an AI cloud with on-demand, reserved, and batch processing options. The goal is to accelerate AI research and promote social implementation of AI technologies.
Hadoop 2
This document provides a technical introduction to Hadoop, including:
- Hadoop has been tested on a 4000 node cluster with 32,000 cores and 16 petabytes of storage.
- Key Hadoop concepts are explained, including jobs, tasks, task attempts, mappers, reducers, and the JobTracker and TaskTrackers.
- The process of launching a MapReduce job is described, from the client submitting the job to the JobTracker distributing tasks to TaskTrackers and running the user-defined mapper and reducer classes.
20181116 Massive Log Processing using I/O optimized PostgreSQL
The document describes a technology called PG-Strom that uses GPU acceleration to optimize I/O performance for PostgreSQL. PG-Strom allows data to be transferred directly from NVMe SSDs to the GPU over the PCIe bus, bypassing the CPU and RAM. This reduces data movement and allows PostgreSQL queries to be partially executed directly on the GPU. Benchmark results show the approach can achieve throughput close to the theoretical hardware limits for a single server configuration processing large datasets.
What's hot (19)
Analytics at Speed: Introduction to ClickHouse and Common Use Cases. By Mikha...
Analytics at Speed: Introduction to ClickHouse and Common Use Cases. By Mikha...
ABCI: An Open Innovation Platform for Advancing AI Research and Deployment
ABCI: An Open Innovation Platform for Advancing AI Research and Deployment
20181116 Massive Log Processing using I/O optimized PostgreSQL
20181116 Massive Log Processing using I/O optimized PostgreSQL
Viewers also liked
The Galaxy toolshed
This presentation provides some technical details on the function of the Galaxy toolshed. It was prepared for a group (Biobix at UGent), during my previous job.
Cell Biology Lecture #2
This document provides an overview of advanced cell biology topics covered in a 2014 semester course, including introduction, research strategies, organelles, membranes, trafficking, signaling, cytoskeleton, and cell cycle. It also discusses methods for studying cells, such as determining component lists through expression profiling, proteomics, localization techniques like immunofluorescence, and perturbation methods like RNAi and CRISPR/Cas9 knockouts. Microscopy techniques like fluorescence, confocal, and live cell imaging are explained. The document concludes with an overview of biochemical characterization and protein structure determination using X-ray crystallography.
DTC Companies List
Direct-to-Consumer Genetic Testing Companies is a document that lists various genetic tests that can be obtained directly from companies without a doctor. It includes tests related to health conditions like cardiovascular disease, diabetes, osteoporosis, cancer, and neurological disorders. The document also lists tests for traits like athletic performance and tests targeted at certain ethnic populations like those of Ashkenazi Jewish descent.
Linux Cluster and Distributed Resource Manager
This document describes a Linux cluster with 3 machines - KHAN, KGENE, and BIO - located at the Center for Genome Science, NIH, and KCDC. It provides details on the KHAN machine which has 94 nodes with various CPU and RAM specifications. It also discusses distributed resource managers which can distribute jobs, provide job scheduling, and report on job, node, and user statuses. Finally, it presents an example of submitting 1,000 serial jobs as a job array to the LoadLeveler distributed resource manager to process 1,000 large input data sets.
역전야매액틴
Actin exists in two forms: monomeric G-actin and filamentous F-actin. G-actin monomers polymerize to form F-actin filaments. F-actin filaments are essential components of the cytoskeleton that give cells their shape and allow cellular movement.
Rocket Launcher Mechanism of Collaborative Actin Assembly Defined by Single-M...
This document summarizes a presentation on Adenomanous Polypolis Coli (APC) and its role in the Wnt signaling pathway and actin assembly. APC is a main component of the Wnt signaling pathway that regulates β-catenin levels and target gene expression. Mutations in APC are associated with colorectal cancers. The presentation shows that APC can directly nucleate actin assembly through its C-terminal basic domain, without sequence homology to known actin binding domains. APC and mDia1 synergistically promote actin assembly in the presence of profilin and capping protein. While the formin mDia1 remains processively bound to actin filament bar
Goode
The document summarizes research presented on the role of Adenomanous Polyposis Coli (APC) in regulating actin assembly. APC is a key component of the Wnt signaling pathway and acts as a tumor suppressor. When mutations occur in the APC gene, it can lead to familial adenomatous polyposis. Research shows that APC directly nucleates actin assembly through its C-terminal basic domain without any known actin-binding motifs. Studies using fluorescence probes indicate that unlike other actin nucleators like Arp2/3 or formins, APC remains bound to the pointed end of actin filaments during nucleation and elongation.
Blaas nieuw leven in je PC met Linux
Deze slides introduceren linux. Waarom Linux? Linux is een volwaardig, veilig en stabiel alternatief voor Windows XP.
Voor hulp, contacteer me op http://www.jakonix.be. Veel succes!
Genome Wide SNP Analysis for Inferring the Population Structure and Genetic H...
Study of genome-wide SNPs, mitochondrial DNA and Y-chromosomal DNA variation can provide a valuable information about the population structure and peopling of human populations. To explain a genetic homogeneity of Koreans and population structure of Koreans and the East Asian populations, we analyzed 153 individuals from the Korea and 77 individuals from the East Asia at 46,559 common single-nucleotide polymorphic loci. The 137 CHB and 113 JPT individuals at 25,769 common SNPs from the International HapMap project were further analyzed to reveal the population structure of the East Asians. Principal Component analyses (PCA) and population differentiation ( ) are examined. In the PCA test, the Jeju individuals were slightly different from other Koreans but their values were not significant. This reflect the genetic homogeneity of Korea population. In general, all the individual samples studied here were clustered into subset of ethnic origin according to their geographical location except Mongolians. Whole genome sequencing of Koreans and other population genome by next generation sequencing technology will provide great opportunity to understand the population expansion and peopling of Korea better.
Cell biology Lecture 6
This document provides an overview of the topics covered in an advanced cell biology course, including cell signaling pathways. The course covers cell structures like the nucleus and membranes, as well as processes like transcription, splicing, trafficking and the cell cycle. It focuses on how cells use receptor proteins and second messengers to transduce chemical and physical stimuli into responses that regulate gene expression, metabolism, secretion and motility. Specific signaling pathways discussed include G protein-coupled receptors, receptor tyrosine kinases, cytokine receptors, MAP kinase pathways, insulin signaling, TGF-beta signaling, calcium signaling and visual transduction in vertebrates.
Cell culture 05
This document contains the syllabus for an advanced animal cell culture course offered in the second semester of 2013 at Chungbuk National University's Department of Animal Science. The syllabus lists 8 topics that will be covered across 8 class meetings from September to December, including introductions to cell culture, stem cells, transgenic animals, and genome engineering. It also provides details on assigned student presentations and the date of the final exam.
Part 6 of RNA-seq for DE analysis: Detecting biology from differential expres...
This presentation discusses RNA-seq analysis for detecting differentially expressed genes. It covers analyzing results from the DESeq tool, setting cut-offs, and performing gene set enrichment analysis using both contingency table approaches and cutoff-free approaches like GSEA. GSEA ranks all genes based on surprise value rather than using a cutoff, and identifies enriched gene sets based on running sum analysis rather than comparing counts in sets. Variations like Piano provide consensus from multiple GSEA methods. Directionality of expression changes within gene sets can also be incorporated.
Cell biology Lecture 5
1. Membrane trafficking allows the transfer of cargo between organelles through transport vesicles that form and fuse with target membranes.
2. Transport vesicles are coated with protein complexes that help generate the vesicles and select cargo for transport. Vesicles move cargo between organelles like the ER, Golgi apparatus, and endosomes.
3. Rab GTPases and SNARE proteins ensure vesicles dock and fuse with the correct target membrane, delivering cargo to its destination compartment.
Viewers also liked (20)
Rocket Launcher Mechanism of Collaborative Actin Assembly Defined by Single-M...
Rocket Launcher Mechanism of Collaborative Actin Assembly Defined by Single-M...
Genome Wide SNP Analysis for Inferring the Population Structure and Genetic H...
Genome Wide SNP Analysis for Inferring the Population Structure and Genetic H...
Part 6 of RNA-seq for DE analysis: Detecting biology from differential expres...
Part 6 of RNA-seq for DE analysis: Detecting biology from differential expres...
Similar to Cluster Drm
BioPig for scalable analysis of big sequencing data
This document introduces BioPig, a Hadoop-based analytic toolkit for large-scale genomic sequence analysis. BioPig aims to provide a flexible, high-level, and scalable platform to enable domain experts to build custom analysis pipelines. It leverages Hadoop's data parallelism to speed up bioinformatics tasks like k-mer counting and assembly. The document demonstrates how BioPig can analyze over 1 terabase of metagenomic data using just 7 lines of code, much more simply than alternative MPI-based solutions. While challenges remain around optimization and integration, BioPig shows promise for scalable genomic analytics on very large datasets.
Distributed caching-computing v3.8
Thinking Distributed: The Hazelcast Way document discusses Hazelcast, an in-memory data grid that provides distributed computing capabilities. It describes how Hazelcast enables scale-out computing, resilience to failures, and an easy programming model. It also outlines Hazelcast's features such as fast performance, persistence, SQL queries, and support for various APIs and languages.
Top 5 mistakes when writing Spark applications
This document discusses common mistakes made when writing Spark applications and provides recommendations to address them. It covers issues like having executors that are too small or large, shuffle blocks exceeding size limits, data skew slowing jobs, and excessive stages. The key recommendations are to optimize executor and partition sizes, increase partitions to reduce skew, use techniques like salting to address skew, and favor transformations like ReduceByKey over GroupByKey to minimize shuffles and memory usage.
Hadoop - Introduction to HDFS
This presentation will give you Information about :
1.Configuring HDFS
2.Interacting With HDFS
3.HDFS Permissions and Security
4.Additional HDFS Tasks
HDFS Overview and Architecture
5.HDFS Installation
6.Hadoop File System Shell
7.File System Java API
Machine learning at Scale with Apache Spark
This document discusses scaling machine learning using Apache Spark. It covers several key topics:
1) Parallelizing machine learning algorithms and neural networks to distribute computation across clusters. This includes data, model, and parameter server parallelism.
2) Apache Spark's Resilient Distributed Datasets (RDDs) programming model which allows distributing data and computation across a cluster in a fault-tolerant manner.
3) Examples of very large neural networks trained on clusters, such as a Google face detection model using 1,000 servers and a IBM brain-inspired chip model using 262,144 CPUs.
Manycores for the Masses
NERSC is the production high-performance computing (HPC) center for the United States Department of Energy (DOE) Office of Science. The center supports over 6,000 users in 600 projects, using a variety of applications in materials science, chemistry, biology, astrophysics, high energy physics, climate science, fusion science, and more.
NERSC deployed the Cori system on over 9,000 Intel® Xeon Phi™ processors. This session describes the optimization strategy for porting codes that target traditional manycore architectures to the processors. We also discuss highlights and lessons learned from the optimization process on 20 applications associated with the NERSC Exascale Science Application Program (NESAP).
2018 03 25 system ml ai and openpower meetup
SystemML is an Apache project that provides a declarative machine learning language for data scientists. It aims to simplify the development of custom machine learning algorithms and enable scalable execution on everything from single nodes to clusters. SystemML provides pre-implemented machine learning algorithms, APIs for various languages, and a cost-based optimizer to compile execution plans tailored to workload and hardware characteristics in order to maximize performance.
High Performance Data Analytics with Java on Large Multicore HPC Clusters
Within the last few years, there have been significant contributions to Java-based big data frameworks and libraries
such as Apache Hadoop, Spark, and Storm. While these
systems are rich in interoperability and features, developing
high performance big data analytic applications is challenging.
Also, the study of performance characteristics and
high performance optimizations is lacking in the literature for
these applications. By contrast, these features are well documented in the High Performance Computing (HPC) domain and some of the techniques have potential performance benefits in the big data domain as well. This paper presents the implementation of a high performance big data analytics library - SPIDAL Java - with a comprehensive discussion on five performance challenges, solutions, and speedup results. SPIDAL Java captures a class of global machine learning applications with significant computation and communication that can serve as a yardstick in studying performance bottlenecks with Java big data analytics. The five challenges present here are the cost of intra-node messaging, inefficient cache utilization, performance costs with threads, overhead of garbage collection, and the costs of heap allocated objects. SPIDAL Java presents its solutions to these and demonstrates significant performance gains and scalability when running on up to 3072 cores in one of the latest Intel Haswell-based multicore clusters.
Spidal Java: High Performance Data Analytics with Java on Large Multicore HPC...
Within the last few years, there have been significant contributions to Java-based big data frameworks and libraries such as Apache Hadoop, Spark, and Storm. While these systems are rich in interoperability and features, developing high performance big data analytic applications is challenging. Also, the study of performance characteristics and high performance optimizations is lacking in the literature for these applications. By contrast, these features are well documented in the High Performance Computing (HPC) domain and some of the techniques have potential performance benefits in the big data domain as well. This paper identifies a class of machine learning applications with significant computation and communication as a yardstick and presents five optimizations to yield high performance in Java big data analytics. Also, it incorporates these optimizations in developing SPIDAL Java - a highly optimized suite of Global Machine Learning (GML) applications. The optimizations include intra-node messaging through memory maps over network calls, improving cache utilization, reliance on processes over threads, zero garbage collection, and employing offheap buffers to load and communicate data. SPIDAL Java demonstrates significant performance gains and scalability with these techniques when running on up to 3072 cores in one of the latest Intel Haswell-based multicore clusters.
http://dsc.soic.indiana.edu/publications/hpc2016-spidal-high-performance-submit-18-public.pdf
http://dsc.soic.indiana.edu/presentations/SPIDALJava.pptx
Gpu Join Presentation
This paper talks about algorithms to do database joins on a GPU. Some interesting work here, that will someday lead to implementing databases on a GPGPU like CUDA.
Hadoop Architecture_Cluster_Cap_Plan
Hadoop is an open-source framework that processes large datasets in a distributed manner across commodity hardware. It uses a distributed file system (HDFS) and MapReduce programming model to store and process data. Hadoop is highly scalable, fault-tolerant, and reliable. It can handle data volumes and variety including structured, semi-structured and unstructured data.
Top 5 mistakes when writing Spark applications
This document discusses common mistakes people make when writing Spark applications and provides recommendations to address them. It covers issues related to executor configuration, application failures due to shuffle block sizes exceeding limits, slow jobs caused by data skew, and managing the DAG to avoid excessive shuffles and stages. Recommendations include using smaller executors, increasing the number of partitions, addressing skew through techniques like salting, and preferring ReduceByKey over GroupByKey and TreeReduce over Reduce to improve performance and resource usage.
Top 5 Mistakes When Writing Spark Applications
This document discusses 5 common mistakes when writing Spark applications:
1) Improperly sizing executors by not considering cores, memory, and overhead. The optimal configuration depends on the workload and cluster resources.
2) Applications failing due to shuffle blocks exceeding 2GB size limit. Increasing the number of partitions helps address this.
3) Jobs running slowly due to data skew in joins and shuffles. Techniques like salting keys can help address skew.
4) Not properly managing the DAG to avoid shuffles and bring work to the data. Using ReduceByKey over GroupByKey and TreeReduce over Reduce when possible.
5) Classpath conflicts arising from mismatched library versions, which can be addressed using sh
Intro to Machine Learning for GPUs
1. The document discusses GPUs and their advantages for machine learning tasks like deep learning and parallel computing. GPUs have many parallel processors that can accelerate matrix multiplications and other computations used in machine learning algorithms.
2. It introduces CUDA and how it allows GPUs to be programmed for general purpose processing through a parallel computing model. Examples are given of how matrix multiplications and convolutional neural network operations can be parallelized on GPUs.
3. H2O is presented as a machine learning platform that supports GPU acceleration for algorithms like gradient boosted machines, enabling faster training on large datasets. Instructions are provided on getting started with CUDA, cuDNN and using GPUs for machine learning.
Advances in GPU Computing
This document summarizes Nvidia's GPU technology conference (GTC16) including announcements about their Tesla P100 GPU and DGX-1 deep learning supercomputer. Key points include:
- The new Tesla P100 GPU delivers up to 21 teraflops of performance for deep learning and uses new technologies like NVLink, HBM2 memory, and a page migration engine.
- The Nvidia DGX-1 is a deep learning supercomputer powered by 8 Tesla P100 GPUs with over 170 teraflops of performance for training neural networks.
- CUDA 8 and unified memory improvements on the P100 enable simpler programming and larger datasets by allowing allocations beyond GPU memory size and
Impala presentation ahad rana
This document provides a high-level overview of Impala, an open-source SQL query engine for Apache Hadoop. It describes how Impala addresses limitations of MapReduce by providing faster, more interactive queries using MPP (Massively Parallel Processing). Key points include that Impala runs directly on data files without ETL, uses a distributed query planner and execution engine for high performance, and supports commonly used file formats like Parquet for columnar storage.
HBaseCon 2015: HBase Operations in a Flurry
With multiple clusters of 1,000+ nodes replicated across multiple data centers, Flurry has learned many operational lessons over the years. In this talk, you'll explore the challenges of maintaining and scaling Flurry's cluster, how we monitor, and how we diagnose and address potential problems.
NAS EP Algorithm
This document summarizes random number generation using OpenCL. It discusses the Marsaglia polar method for generating random numbers and Gaussian pairs. It presents pseudocode for the Gaussian pair generation algorithm. Profiling results show that 54% of time is spent generating Gaussian pairs while 46% is for random numbers. The document also discusses optimization techniques like using local memory, coalesced global memory access, and choosing an optimal work group size. Performance results show near linear speedup from 1 to 8 GPUs.
Distributed caching and computing v3.7
The document discusses Hazelcast, an in-memory data grid platform. Hazelcast provides features like scale-out computing, resilience, fast performance, and an easy programming model. It can be used for distributed caching, computing, messaging, and data storage. Hazelcast runs as a distributed system across multiple nodes and provides APIs for Java and other languages.
Think Distributed: The Hazelcast Way
Hazelcast provides scale-out computing capabilities that allow cluster capacity to be increased or decreased on demand. It enables resilience through automatic recovery from member failures without data loss. Hazelcast's programming model allows developers to easily program cluster applications as if they are a single process. It also provides fast application performance by holding large data sets in main memory.
Similar to Cluster Drm (20)
BioPig for scalable analysis of big sequencing data
BioPig for scalable analysis of big sequencing data
High Performance Data Analytics with Java on Large Multicore HPC Clusters
High Performance Data Analytics with Java on Large Multicore HPC Clusters
Spidal Java: High Performance Data Analytics with Java on Large Multicore HPC...
Spidal Java: High Performance Data Analytics with Java on Large Multicore HPC...
More from Hong ChangBum
Detecting Somatic Mutations in Impure Cancer Sample - Ensemble Approach
The document describes an ensemble approach for detecting somatic mutations in impure cancer samples. It details the benchmark data set used from TCGA, which includes BAM files for a tumor sample that is 50% tumor cells and 50% normal cells mixed. It then outlines several commonly used somatic mutation callers - SomaticSniper, VarScan2, MuTect - and applies them to the data set. The results from the different callers are then combined using various filtering and consensus approaches to generate a high confidence list of somatic mutations for validation.
Detecting Somatic Mutation - Ensemble Approach
This document discusses approaches for identifying somatic mutations from cancer sequencing data using multiple mutation callers. It compares several popular mutation callers, explores a simple consensus approach, and proposes an integrated ensemble approach. The ensemble approach applies multiple callers, filters using GATK, and assigns a ranking score to variants based on validation rates to generate a high-confidence list of somatic mutations. This strategy aims to leverage the strengths of different callers to improve accuracy over any single caller.
Galaxy RNA-Seq Analysis: Tuxedo Protocol
This document provides an overview of analyzing RNA-Seq data using the Tuxedo protocol in Galaxy. It describes experimental design considerations, quality control of sequencing data using FastQC, mapping reads to a reference genome using Tophat, determining differential expression with Cuffdiff, and visualizing results using IGV and CummeRbund. The tutorial walks through an example analysis on Drosophila melanogaster RNA-Seq data, covering topics such as setting file formats, running alignment and expression tools, extracting workflows, and useful Galaxy resources.
Workshop 2011
This document provides an overview of using the STRUCTURE software to analyze population structure from genetic data. STRUCTURE is a model-based clustering method that detects underlying genetic populations among individuals genotyped at multiple markers. It takes genotype data as input and computes the proportion of each individual's genome originating from inferred populations. The document discusses input format, parameter configuration, running single and batch runs, and analyzing results to infer the true number of populations.
BioSMACK - Linux Live CD for GWAS
1. BioSMACK is a Linux Live CD customized for analysis of genome-wide association studies (GWAS).
2. It provides pre-compiled, installed and configured software for GWAS analysis like PLINK, EIGENSTRAT, STRUCTURE, and others from a bootable CD/USB without installing on the hard disk.
3. Future works include supporting cloud and cluster computing for parallel GWAS analysis on large datasets.
Next-generation genomics: an integrative approach
This document summarizes a presentation on next-generation genomics and integrative analysis. It discusses the types of genomic data available from techniques like genome sequencing, RNA sequencing, ChIP-seq, and epigenomics. It explains that integrative analysis can help annotate functional features, infer variant function, and understand gene regulation. Approaches to integration include data reduction, unsupervised clustering, and supervised Bayesian networks. Large-scale datasets can be accessed through browsers, add-ons, and standalone tools to generate novel hypotheses. Future work includes more integrated community resources with search capabilities.
worldwide population
This document summarizes a study that analyzed population differentiation at disease-associated SNPs in over 1,000 individuals from 53 global populations. The study genotyped 25 SNPs related to 6 complex human diseases in populations from 7 geographic regions. It found genetic differentiation between populations, with some SNPs showing strong differentiation within East Asia or between Korea and Jeju Island. Allele frequencies of disease SNPs varied between populations and were not always predictive between races. The study also examined selection resources to identify signals of selection, like thicker hair, and found differences in selected regions between East Asia, Middle East and Europe. It concluded that analyzing a large number of populations can help replicate findings and better characterize allele frequency differences globally.
Perspectives of identifying Korean genetic variations
This document summarizes research on identifying genetic variations in the Korean population using single-nucleotide polymorphisms (SNPs). It describes analyzing the population structure based on SNP genotypes, using SNPs to determine kinship, and identifying monozygotic twins using copy number variations (CNVs). It also discusses using SNPs to study physical traits in Koreans and developing ancestry informative markers and a database of genomic variants for Korea.
Genome Browser based on Google Maps API
The document discusses using the Google Maps API to embed maps on web pages. It allows users to manipulate maps and add content. Key details include how to position maps using longitude and latitude coordinates, how genomic sequence data is tiled into images at different zoom levels, and how those images are named algorithmically based on their position. Components like panels and a search box for interacting with the embedded map are also mentioned.
Korean Database of Genomic Variants
The document describes the Korean Database of Genomic Variants (KDGV), which is available online at http://ksnp.cdc.go.kr. It was established in 2009 to catalog genomic variants in the Korean population. The KDGV integrates data from other Korean genomic databases including KSNP, KARE, and GWASaid to provide information on SNPs, genes, and genomic regions of interest. It allows users to search and browse genomic data through the customizable genome browser interface.
Dt Ccompanieslist
This document provides an overview of various direct-to-consumer genetic testing companies and the types of genetic tests they offer. It shows that 23andMe, DeCODE, Navigenics, and Knome offer the widest range of tests related to health, traits, and ancestry. Most companies focus on specific health conditions like cancer, cardiovascular disease, or pharmacogenomics. The tests range from single gene tests to more comprehensive scans of parts of the genome.
My Project
This document discusses genomic variation research and personalized medicine. It mentions tools used for sequence alignment, database management, text analysis, and machine learning algorithms. Genome browsers and clusters are referenced as computational resources. Challenges around analyzing large genomic datasets within reasonable timeframes are also noted.
Genome Browser
This document discusses GenomeBrowser. It mentions that the UCSC Human Genome Browser receives 50,000 hits per day and 3,000 users per day, while another receives 1,257 hits per day and 10 users per day. It also discusses various features of GenomeBrowser like being lightweight, configurable, and promoting data sharing.
GenomeBrowser
The document discusses Genome Browser, a lightweight and portable genome browsing software. It can be configured and customized easily. It promotes data sharing through tracks and supports the Distributed Annotation System (DAS) for adding third-party data. The software uses common bioinformatics libraries and has easy installation methods for various operating systems.
Next Generation bio Research Infra
The document discusses project management tools for developers including a developer blog, version control systems like SVN and issue tracking systems like trac. It also mentions tools for documentation projects like moniWiki and describes using FTP for file transfers.
More from Hong ChangBum (20)
Detecting Somatic Mutations in Impure Cancer Sample - Ensemble Approach
Detecting Somatic Mutations in Impure Cancer Sample - Ensemble Approach
Perspectives of identifying Korean genetic variations
Perspectives of identifying Korean genetic variations
Recently uploaded
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
DevOps and Testing slides at DASA Connect
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
UiPath Test Automation using UiPath Test Suite series, part 5
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
GridMate - End to end testing is a critical piece to ensure quality and avoid...
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Recently uploaded (20)
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
GridMate - End to end testing is a critical piece to ensure quality and avoid...
GridMate - End to end testing is a critical piece to ensure quality and avoid...
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Cluster Drm
- 1. Linux Cluster and Distributed Resource Manager Center for Genome Science, NIH, KCDC Hong Chang Bum hongiiv@gmail.com
- 2. . .
- 4. Compute Intensive Data/Memory Intensive High Throughput Large Simulation 64 bit address space shared memory parallelism all good but....think basic ^^
- 5. THE RETURN OF THE SERIAL PROGRAM
- 6. Linux Cluster • 3 Linux Cluster Machine • KHAN (192.168.100.245, PPC, 94 Node) • KGENE (192.168.100.205, X86, 28 Node) • LOGIN (192.168.100.208, 8GB RAM, IA64) • LOGINDB (192.168.100.207, 12GB RAM, IA64) • DEV(192.168.100.226, 6GB RAM X86)