Full Webinar: https://info.tigergraph.com/graph-gurus-35
By attending this webinar you will:
-Learn how to use TigerGraph’s no-code capabilities;
-Understand how TigerGraph is built for scale and performance;
-Get a deep dive into TigerGraph 3.0 feature enhancements.
Graph Gurus Episode 37: Modeling for Kaggle COVID-19 DatasetTigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-37
In this Graph Gurus Episode, we:
-Learn how to process text and extract entities (words and phrases) as well as classes linking the entities using SciSpacy, a Natural Language Processing (NLP) tool.
-Import the output of NLP and semantically link it in TigerGraph
-Run advanced analytics queries with TigerGraph to analyze the relationships and deliver insights
Graph Gurus Episode 25: Unleash the Business Value of Your Data Lake with Gra...TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-25
A new weapon is available for businesses wanting to accomplish more with Hadoop: native parallel graphs can reveal the connections across multiple domains and datasets in data lakes and provide powerful insights to deliver superior outcomes. In this webinar we will explain how native parallel graphs can analyze the information in data lakes to enable the following outcomes:
Recommending next best actions such as promoting a student loan to someone heading off to college, advocating life insurance to a newly married couple, and so on
Improving network utilization by analyzing petabytes of data collected from millions of IoT devices across a smart grid
Accelerating M&A activity by intelligently merging data lakes from multiple businesses.
Full Webinar: https://info.tigergraph.com/graph-gurus-21
In this Graph Gurus episode, we:
Explain the architecture and technical implementation for a TigerGraph + Spark graph-enhanced Machine Learning pipeline
Use TigerGraph both before training to extract (graph and non-graph) features and after training to apply the model on streaming data
Use Spark to train and tune machine learning models at scale
Present a solution in production at China Mobile that detects and prevents phone-based scams using machine learning with TigerGraph
Demo the data flow between Spark and TigerGraph via TigerGraph’s JDBC driver
Graph Gurus Episode 37: Modeling for Kaggle COVID-19 DatasetTigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-37
In this Graph Gurus Episode, we:
-Learn how to process text and extract entities (words and phrases) as well as classes linking the entities using SciSpacy, a Natural Language Processing (NLP) tool.
-Import the output of NLP and semantically link it in TigerGraph
-Run advanced analytics queries with TigerGraph to analyze the relationships and deliver insights
Graph Gurus Episode 25: Unleash the Business Value of Your Data Lake with Gra...TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-25
A new weapon is available for businesses wanting to accomplish more with Hadoop: native parallel graphs can reveal the connections across multiple domains and datasets in data lakes and provide powerful insights to deliver superior outcomes. In this webinar we will explain how native parallel graphs can analyze the information in data lakes to enable the following outcomes:
Recommending next best actions such as promoting a student loan to someone heading off to college, advocating life insurance to a newly married couple, and so on
Improving network utilization by analyzing petabytes of data collected from millions of IoT devices across a smart grid
Accelerating M&A activity by intelligently merging data lakes from multiple businesses.
Full Webinar: https://info.tigergraph.com/graph-gurus-21
In this Graph Gurus episode, we:
Explain the architecture and technical implementation for a TigerGraph + Spark graph-enhanced Machine Learning pipeline
Use TigerGraph both before training to extract (graph and non-graph) features and after training to apply the model on streaming data
Use Spark to train and tune machine learning models at scale
Present a solution in production at China Mobile that detects and prevents phone-based scams using machine learning with TigerGraph
Demo the data flow between Spark and TigerGraph via TigerGraph’s JDBC driver
Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-26
Have you ever wondered how routing apps like Google Maps find the best route from one place to another? Finding that route is solved by the Shortest Path graph algorithm. Today, graph algorithms are moving from the classroom to a host of important and valuable operational and analytical applications. This webinar will give you an overview of graph algorithms, how to use them, and the categories of problems they can solve, and then take a closer look at path algorithms. This webinar is the first part in a five-part series, each part examining a different type of problem to be solved.
Graph Gurus Episode 27: Using Graph Algorithms for Advanced Analytics Part 2TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-27
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms. Join us for Part 2 of our five-part webinar series on using graph algorithms for advanced analytics.
By attending this webinar you will:
- Hear about use cases for centrality graph algorithms
- Learn how to select the right algorithm for your use case
- Be able to run and tailor GSQL graph algorithms
Using Graph Algorithms for Advanced Analytics - Part 2 CentralityTigerGraph
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
Graph Databases and Machine Learning | November 2018TigerGraph
Graph Database and Machine Learning: Finding a Happy Marriage. Graph Databases and Machine Learning
both represent powerful tools for getting more value from data, learn how they can form a harmonious marriage to up-level machine learning.
Using Graph Algorithms For Advanced Analytics - Part 4 Similarity 30 graph al...TigerGraph
Graph-based investigation often enables us to identify individuals who are of special interest, and their uniqueness is due in part to their pattern of interactions. For example:
-A patient whose carepath journey leverages best-practices gained from using pattern matching algorithms that find similar issues among the data of 50 million patients
-An individual who builds a successful portfolio by implementing actions recommended by similarity algorithms that find equivalent actions by successful investors
-A participant in a criminal ring whose attempts at swindling are blocked by matching them to patterns of known fraudulent activity
Once you have identified such a pattern and a key individual, you want to search your data for similar occurrences. Similarity algorithms are the answer.
Graph Gurus Episode 17: Seven Key Data Science Capabilities Powered by a Nati...TigerGraph
This webinar will demonstrate seven key data science capabilities using TigerGraph’s intuitive GUI, GraphStudio and GSQL queries. In this episode, we:
-Share the capabilities and tie those to specific use cases across healthcare, pharmaceutical, financial services, Telecom, Internet and government industries.
-Walk you through a sample dataset, GraphStudio UI flow, and GSQL queries demonstrating the capabilities.
-Cover client case studies for Amgen, Intuit, China Mobile, Santa Clara County, and other enterprise customers
Full Webinar: https://info.tigergraph.com/graph-gurus-28
In this webinar, we will use the recommendation system problem, which can be efficiently solved as a graph problem, to demonstrate the in-database training capability of TigerGraph, a native graph database. A hybrid (memory-based + model-based) recommendation system will be implemented in TigerGraph. Specifically, the latent factor model used for recommendation will be trained within the database.
In this Graph Gurus episode, we will:
-Review multiple widely-used recommendation methods
-Introduce the concept of in-database machine learning
-Present an in-database machine learning solution for a real time recommendation system
Using Graph Algorithms for Advanced Analytics - Part 2 CentralityTigerGraph
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
Using Graph Algorithms for Advanced Analytics - Part 5 ClassificationTigerGraph
What atmospheric data will help you predict if it's going to rain, snow, or be windy? What position should that new athlete play? How well can you guess a person's demographic background, based on their chat activity? These are all classification problems -- trying to pick the right category or label for an entity, based on observable features. They can also be solved with machine learning.
Comparing three data ingestion approaches where Apache Kafka integrates with ...HostedbyConfluent
Using Kafka to stream data into TigerGraph, a distributed graph database, is a common pattern in our customers’ data architecture. We have seen the integration in three different layers around TigerGraph’s data flow architecture, and many key use case areas such as customer 360, entity resolution, fraud detection, machine learning, and recommendation engine. Firstly, TigerGraph’s internal data ingestion architecture relies on Kafka as an internal component. Secondly, TigerGraph has a builtin Kafka Loader, which can connect directly with an external Kafka cluster for data streaming. Thirdly, users can use an external Kafka cluster to connect other cloud data sources to TigerGraph cloud database solutions through the built-in Kafka Loader feature. In this session, we will present the high-level architecture in three different approaches and demo the data streaming process.
Peek into Neo4j Product Strategy and Roadmap
Anurag Tandon, VP Product Management, Neo4j
Get a sneak peek into recent product enhancements and some exciting announcements. We will discuss the three main pillars of ongoing product strategy at Neo4j and briefly touch on important 2024 initiatives.
OSMC 2023 | What’s new with Grafana Labs’s Open Source Observability stack by...NETWAYS
Open source is at the heart of what we do at Grafana Labs and there is so much happening! The intent of this talk to update everyone on the latest development when it comes to Grafana, Pyroscope, Faro, Loki, Mimir, Tempo and more. Everyone has had at least heard about Grafana but maybe some of the other projects mentioned above are new to you? Welcome to this talk 😉 Beside the update what is new we will also quickly introduce them during this talk.
Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-26
Have you ever wondered how routing apps like Google Maps find the best route from one place to another? Finding that route is solved by the Shortest Path graph algorithm. Today, graph algorithms are moving from the classroom to a host of important and valuable operational and analytical applications. This webinar will give you an overview of graph algorithms, how to use them, and the categories of problems they can solve, and then take a closer look at path algorithms. This webinar is the first part in a five-part series, each part examining a different type of problem to be solved.
Graph Gurus Episode 27: Using Graph Algorithms for Advanced Analytics Part 2TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-27
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms. Join us for Part 2 of our five-part webinar series on using graph algorithms for advanced analytics.
By attending this webinar you will:
- Hear about use cases for centrality graph algorithms
- Learn how to select the right algorithm for your use case
- Be able to run and tailor GSQL graph algorithms
Using Graph Algorithms for Advanced Analytics - Part 2 CentralityTigerGraph
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
Graph Databases and Machine Learning | November 2018TigerGraph
Graph Database and Machine Learning: Finding a Happy Marriage. Graph Databases and Machine Learning
both represent powerful tools for getting more value from data, learn how they can form a harmonious marriage to up-level machine learning.
Using Graph Algorithms For Advanced Analytics - Part 4 Similarity 30 graph al...TigerGraph
Graph-based investigation often enables us to identify individuals who are of special interest, and their uniqueness is due in part to their pattern of interactions. For example:
-A patient whose carepath journey leverages best-practices gained from using pattern matching algorithms that find similar issues among the data of 50 million patients
-An individual who builds a successful portfolio by implementing actions recommended by similarity algorithms that find equivalent actions by successful investors
-A participant in a criminal ring whose attempts at swindling are blocked by matching them to patterns of known fraudulent activity
Once you have identified such a pattern and a key individual, you want to search your data for similar occurrences. Similarity algorithms are the answer.
Graph Gurus Episode 17: Seven Key Data Science Capabilities Powered by a Nati...TigerGraph
This webinar will demonstrate seven key data science capabilities using TigerGraph’s intuitive GUI, GraphStudio and GSQL queries. In this episode, we:
-Share the capabilities and tie those to specific use cases across healthcare, pharmaceutical, financial services, Telecom, Internet and government industries.
-Walk you through a sample dataset, GraphStudio UI flow, and GSQL queries demonstrating the capabilities.
-Cover client case studies for Amgen, Intuit, China Mobile, Santa Clara County, and other enterprise customers
Full Webinar: https://info.tigergraph.com/graph-gurus-28
In this webinar, we will use the recommendation system problem, which can be efficiently solved as a graph problem, to demonstrate the in-database training capability of TigerGraph, a native graph database. A hybrid (memory-based + model-based) recommendation system will be implemented in TigerGraph. Specifically, the latent factor model used for recommendation will be trained within the database.
In this Graph Gurus episode, we will:
-Review multiple widely-used recommendation methods
-Introduce the concept of in-database machine learning
-Present an in-database machine learning solution for a real time recommendation system
Using Graph Algorithms for Advanced Analytics - Part 2 CentralityTigerGraph
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
Using Graph Algorithms for Advanced Analytics - Part 5 ClassificationTigerGraph
What atmospheric data will help you predict if it's going to rain, snow, or be windy? What position should that new athlete play? How well can you guess a person's demographic background, based on their chat activity? These are all classification problems -- trying to pick the right category or label for an entity, based on observable features. They can also be solved with machine learning.
Comparing three data ingestion approaches where Apache Kafka integrates with ...HostedbyConfluent
Using Kafka to stream data into TigerGraph, a distributed graph database, is a common pattern in our customers’ data architecture. We have seen the integration in three different layers around TigerGraph’s data flow architecture, and many key use case areas such as customer 360, entity resolution, fraud detection, machine learning, and recommendation engine. Firstly, TigerGraph’s internal data ingestion architecture relies on Kafka as an internal component. Secondly, TigerGraph has a builtin Kafka Loader, which can connect directly with an external Kafka cluster for data streaming. Thirdly, users can use an external Kafka cluster to connect other cloud data sources to TigerGraph cloud database solutions through the built-in Kafka Loader feature. In this session, we will present the high-level architecture in three different approaches and demo the data streaming process.
Peek into Neo4j Product Strategy and Roadmap
Anurag Tandon, VP Product Management, Neo4j
Get a sneak peek into recent product enhancements and some exciting announcements. We will discuss the three main pillars of ongoing product strategy at Neo4j and briefly touch on important 2024 initiatives.
OSMC 2023 | What’s new with Grafana Labs’s Open Source Observability stack by...NETWAYS
Open source is at the heart of what we do at Grafana Labs and there is so much happening! The intent of this talk to update everyone on the latest development when it comes to Grafana, Pyroscope, Faro, Loki, Mimir, Tempo and more. Everyone has had at least heard about Grafana but maybe some of the other projects mentioned above are new to you? Welcome to this talk 😉 Beside the update what is new we will also quickly introduce them during this talk.
Advanced technologies and techniques for debugging HPC applicationsRogue Wave Software
Presented at Supercomputing 18. Debugging and analyzing today's HPC applications requires a tool with capabilities and features to support the demands of today’s complex HPC applications. Debugging tools must be able to handle the extensive use of C++ templates and the STL, use of many shared libraries, optimized code, code leveraging GPU accelerators and applications constructed with multiple languages.
This presentation walks through the different advanced technologies provided by the debugger, TotalView for HPC, and shows how they can be used to easily understand complex code and quickly solve difficult problems. Showcasing TotalView’s new user interface, you will learn how to leverage the amazing technology of reverse debugging to replay how your program ran. You will also see how TotalView provides a unified view across applications that utilize Python and C++, debug CUDA applications, find memory leaks in your HPC codes and other powerful techniques for improving the quality of your code.
How a distributed graph analytics platform uses Apache Kafka for data ingesti...HostedbyConfluent
Using Kafka to stream data into TigerGraph, a distributed graph database, is a common pattern in our customers’ data architecture. In the TigerGraph database, Kafka Connect framework was used to build the native S3 data loader. In TigerGraph Cloud, we will be building native integration with many data sources such as Azure Blob Storage and Google Cloud Storage using Kafka as an integrated component for the Cloud Portal.
In this session, we will be discussing both architectures: 1. built-in Kafka Connect framework within TigerGraph database; 2. using Kafka cluster for cloud native integration with other popular data sources. Demo will be provided for both data streaming processes.
Apache AGE and the synergy effect in the combination of Postgres and NoSQLEDB
In this session, we will introduce the concept of Apache AGE and the synergy effect in the combination of Postgres and NoSQL (Graph Database). We shall discuss the story and background of Apache AGE as an open-source project and introduce challenges that AGE can solve for its users. Furthermore, we will talk about a graph database as an extension to PostgreSQL and how it can support all the functionalities and features of PostgreSQL and offers a graph model in addition. We will also discuss how users with a relational background and data model who are in need of having a graph model on top of their existing relational model, can use this extension with minimal effort because they can use existing data without migration to enable a graph database.
MySQL Applier for Apache Hadoop: Real-Time Event Streaming to HDFSMats Kindahl
This presentation from MySQL Connect give a brief introduction to Big Data and the tooling used to gain insights into your data. It also introduces an experimental prototype of the MySQL Applier for Hadoop which can be used to incorporate changes from MySQL into HDFS using the replication protocol.
Dagster - DataOps and MLOps for Machine Learning Engineers.pdfHong Ong
In this session, we will introduce Dagster, a cutting-edge framework that simplifies DataOps and MLOps for machine learning engineers. We will explore the benefits of this powerful tool, learn how to implement it in your machine learning workflows, and discuss practical use cases to help you enhance productivity, collaboration, and deployment of ML models.
The highlights of this presentation featuring Postgres Enterprise Manager 4.0 include:
• Perfecting your Performance with advanced features such as performance home pages, SQL Profiler, Index Advisor, Postgres Expert, and Tuning Wizard.
• Capacity Planning and Forecasting by automating the collection of your key performance statistics, customizing metrics and reports to analyze historical trend analysis.
• How to Script Less and Monitor More with a really cool graphical interface that provides a fast and consistent method of working with database probes, alerts and various task managers simultaneously.
• Setting up your Customizable Dashboards that consolidate and display all your data with at-a-glance visualization tools in both a platform specific client or a web client.
Please visit http://www.Enterprisedb.com/pem for more information.
Deep learning beyond the learning - Jörg Schad - Codemotion Amsterdam 2018Codemotion
Open Source frameworks such as TensorFlow, MXNet, or PyTorch enable anyone to model and train Deep Neural Networks. While there are many great tutorials and talks showing us the best ways for training models, there is few information on what happens after we have trained our model? How can we store, utilize, and update it? In this talk, we look at the complete Deep Learning Pipeline and looks at topics such as deployments, multi-tenancy, jupyter notebooks, model serving, and more.
GoodData: The DevOps Story @ FIT CVUT October 16 2013Jaroslav Gergic
Presentation was a part of FIT CVUT / MI-AIT (Případové studie aplikace a řízení IT).
We compare the traditional organization model of separate teams for engineering, QA and operations to the DevOps model using autonomous cross-functional teams. The presentation uses GoodData as a case study.
New Performance Benchmarks: Apache Impala (incubating) Leads Traditional Anal...Cloudera, Inc.
Recording Link: http://bit.ly/LSImpala
Author: Greg Rahn, Cloudera Director of Product Management
In this session, we'll review the recent set of benchmark tests the Apache Impala (incubating) performance team completed that compare Apache Impala to a traditional analytic database (Greenplum), as well as to other SQL-on-Hadoop engines (Hive LLAP, Spark SQL, and Presto). We'll go over the methodology and results, and we'll also discuss some of the performance features and best practices that make this performance possible in Impala. Lastly, we'll look at some recent advancements in in Impala over the past few releases.
Discover How IBM Uses InfluxDB and Grafana to Help Clients Monitor Large Prod...InfluxData
IBM has been innovating to create new products for its clients and the world for over a century. Customers look to IBM Power Systems to address their hybrid multicloud infrastructure needs. Larger POWER9 servers can have up to 192 CPU cores, 64 TB of memory, dozens of PB of SAN storage, and typically run a mixture of AIX (UNIX) and Enterprise Linux (RHEL or SLES) workloads. As part of its sales process, IBM is always benchmarking its new hardware and software which clients use to monitor their systems. Discover how IBM and its clients are using InfluxDB and Grafana to collect, store and visualize performance data, which is used to monitor and tune for peak performance in ever-changing workload environments.
Join this webinar featuring Nigel Griffiths from IBM, Ronald McCollam from Grafana Labs, and Russ Savage from InfluxData to learn how you can use InfluxDB and Grafana to improve large production workloads. Learn about the latest product updates from InfluxData and Grafana Labs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).