How to continuously improve Apache Pegasus in complex toB scenariosacelyc1112009
?
A presentation in Apache Pegasus meetup in 2022 from Hao Wang.
Apache Pegasus is a horizontally scalable, strongly consistent and high-performance key-value store.
Know more about Pegasus https://pegasus.apache.org, https://github.com/apache/incubator-pegasus
Accelerating EDA workloads on Azure – Best Practice and benchmark on Intel EM...Meng-Ru (Raymond) Tsai
?
Benchmark EDA workloads on Azure Intel Emerald Rapids (EMR) VMs.
The article evaluates the performance of the latest Azure VMs using the 5th Gen Intel? Xeon? Platinum 8537C (Emerald Rapids) processor by comparing them to the previous Ice Lake generation. Using two EDA tools, Cadence Spectre-X and Synopsys VCS, the benchmarks involve real-world scenarios including single-threaded, multi-threaded, and multiple jobs running on one node.
Results show that Spectre-X performs 12 to 18% better on D64ds v6 instances and 22 to 29% better on FX64v2 instances compared to D64ds v5 instances. The D64ds v6 instances were found to be more cost-effective, while FX64mds v2 instances achieved the shortest total runtime. For Synopsys VCS, the benchmarks revealed a speedup of 17 to 43% for Emerald Rapids instances over Ice Lake instances across various parallel simulations. The findings offer EDA customers options on which Azure EMR instances to select based on the cost-efficiency analysis.
How to continuously improve Apache Pegasus in complex toB scenariosacelyc1112009
?
A presentation in Apache Pegasus meetup in 2022 from Hao Wang.
Apache Pegasus is a horizontally scalable, strongly consistent and high-performance key-value store.
Know more about Pegasus https://pegasus.apache.org, https://github.com/apache/incubator-pegasus
Accelerating EDA workloads on Azure – Best Practice and benchmark on Intel EM...Meng-Ru (Raymond) Tsai
?
Benchmark EDA workloads on Azure Intel Emerald Rapids (EMR) VMs.
The article evaluates the performance of the latest Azure VMs using the 5th Gen Intel? Xeon? Platinum 8537C (Emerald Rapids) processor by comparing them to the previous Ice Lake generation. Using two EDA tools, Cadence Spectre-X and Synopsys VCS, the benchmarks involve real-world scenarios including single-threaded, multi-threaded, and multiple jobs running on one node.
Results show that Spectre-X performs 12 to 18% better on D64ds v6 instances and 22 to 29% better on FX64v2 instances compared to D64ds v5 instances. The D64ds v6 instances were found to be more cost-effective, while FX64mds v2 instances achieved the shortest total runtime. For Synopsys VCS, the benchmarks revealed a speedup of 17 to 43% for Emerald Rapids instances over Ice Lake instances across various parallel simulations. The findings offer EDA customers options on which Azure EMR instances to select based on the cost-efficiency analysis.
Introduce Microsoft's Generative AI application tools and provide examples of their use in the medical field. The presentation is given by Raymond Tsai, a Principal Technical Program Manager of Azure HPC & AI Engineering group.
This document discusses using Microsoft Azure cloud computing resources to conduct genome-wide association studies (GWAS) and polygenic risk scoring (PRS) to predict COVID-19 mortality. Key steps include acquiring genotype and phenotype data, performing quality control, running GWAS and PRS analyses using HPC clusters on Azure, and downloading results. Azure provides scalable computing and storage for the large genomic datasets. Its HPC capabilities allow accelerating the analyses, which could otherwise take months to complete on-premises.
This document introduces five key areas of digital economy: artificial intelligence, data science, smart content, smart networking, and digital marketing. It then discusses industry case studies and the capabilities needed in these fields, as well as opportunities and challenges. Raymond Tsai from Microsoft gives an overview of how COVID-19 has accelerated digital transformation, emphasizing how technologies in these five areas can empower employees, engage customers, transform products, and optimize operations.
The document discusses artificial intelligence and its applications. It notes that AI's greatest uses are in prediction, and that predictions do not need to be perfectly accurate to be beneficial. It also mentions privacy, security, and social ethics as important considerations for AI. The document provides examples of AI applications in various industries like manufacturing, retail, banking, healthcare and more. It also gives case studies of how companies like Maersk and 3M are using blockchain and AI technologies to improve processes like maritime insurance and pharmaceutical supply chain management.
This document discusses Raymond Cai, a senior cloud project manager at Microsoft who focuses on Azure. It provides an overview of IaaS, PaaS, and SaaS cloud models and discusses key aspects of public clouds like Azure, AWS, and Google Cloud including their global datacenter locations. It also summarizes Microsoft's global private network that connects their datacenters and the benefits this provides for things like securely transferring large files and globally serving customers.
Microsoft provides various AI and machine learning tools and services. These include cognitive services, bots, Azure Machine Learning, and tools for building, training, deploying and managing AI models. Microsoft's portfolio addresses key questions around what engines to use, deployment targets, and whether to build your own models or consume pre-trained ones. The document also discusses trends in AI adoption like hybrid training/scoring scenarios and pushing inference to edge devices.
This document discusses how AI and human intelligence can work together as a "super doctor" to help identify cancer cells and predict patient prognosis. Deep learning algorithms are applied to process and classify different types of malignant tumor slides. Features engineering and deep learning methods used in ImageNet competitions help process large pathology slides with resolutions up to 40,000x40,000 pixels.
The document discusses the growth of connected devices and the "Internet of Everything", where billions more "things" will be connected to the Internet by 2020. It describes how each connected device is part of a "digital shadow" that can provide contextual information about individuals to enable personalized services. By collecting data from these new sources, systems can gain a better understanding of human needs, behaviors, and intents.
The document discusses Azure Monitor and its built-in monitoring capabilities for Azure resources. It provides out-of-the-box metrics and logs with alert rules to get notified and take automated actions. It allows for platform and application monitoring from a single dashboard view with APIs for third party integration. Links are provided for documentation on supported metrics, monitoring overview, enabling alerts using templates, and best practices for monitoring.
The document discusses Microsoft's Cognitive Services which provide REST APIs for tasks like computer vision, speech recognition, and language processing that can easily be integrated into applications with just a few lines of code; these APIs are built by experts and offer documentation, samples, and community support to help developers find the right API and build quality functionality.
The document discusses DevOps practices for optimizing resources, automating testing, monitoring usage and feedback in production, and adopting a production-first mindset. It outlines a flow of customer value with team autonomy and enterprise alignment, and a backlog refined through learning evidence gathered in production and managed technical debt.
The document discusses Microsoft Bot Framework and its key components for building bots including Bot Builder SDKs, Bot Directory, Bot Connector, and Cognitive Services. It provides an overview of how developers can use the Bot Builder SDKs to build bots, register bots in the Bot Directory to make them discoverable, and connect bots to channels through the Bot Connector. It also mentions capabilities like leveraging Cognitive Services and the ability to analyze customer interactions.
Microsoft's Blockchain as a Service (BaaS) provides a platform for customers and partners to quickly develop, test, and deploy blockchain applications using leading frameworks. It offers ready-made environments to experiment with technologies like Ethereum and Ripple at low cost. BaaS allows blockchain solutions to be exposed globally using Microsoft's worldwide infrastructure and helps partners innovate with blockchain through a mix of technologies.
18. 將資料轉換為智能及決策之 PaaS 服務
Intelligence
Dashboards &
Visualizations
Information
Management
Big Data Stores Machine Learning
and Analytics
CortanaEvent Hubs
HDInsight
(Hadoop and
Spark)
Stream
Analytics
Data Intelligence Action
People
Automated
Systems
Apps
Web
Mobile
Bots
Bot
Framework
SQL Data
WarehouseData Catalog
Data Lake
Analytics
Data Factory
Machine
Learning
Data Lake Store
Cognitive
Services
Power BI
Data
Sources
Apps
Sensors
and
devices
Data
22. 2015 System
Human Error Rate 4%
Speech Recognition could reach human parity in the next 3 years
26. Roll your own with REST APIs
Simple to add: just a few lines of
code required
Integrate into the language and
platform of your choice
Breadth of offerings helps you find the
right API for your app
Built by experts in their field from
Microsoft Research, Bing, and Azure
Machine Learning
Quality documentation, sample
code, and community support
Easy Flexible Tested
GET A
KEY
69. Microsoft Bot Builder (SDKs)
Microsoft Bot Directory
Knowledge & Intelligence Services
VisionWeb Search
Language Speech Knowledge
…ML
Public APIs (Cognitive Services)
Dialog Manager
Knowledge &
Action Graph Entity
Private APIs
Microsoft Bot Connector 4
3
2
1
Add smarts to your bot
Build a great bot
Make your bot discoverable
Connect your bot to channels
ConversationasaPlatform
Conversation as a Platform