DotNet 2019 | Javier Cantón - Writing high performance code in NetCore 3.0

•Télécharger en tant que PPTX, PDF•

1 j'aime•364 vues

This document discusses techniques for writing high performance .NET Core 3.0 code. It covers new features like Span<T>, ValueTuple, and C# 8 async streams. It emphasizes that micro-optimizations are only needed for BCL, real-time apps, and graphics. Bottlenecks follow the Pareto principle. The document then discusses specific optimizations for a KTX file loader, including using stackalloc and unsafe code for pinned memory as well as custom collections and multithreading for OpenGL. It concludes by covering new MathF APIs, hardware intrinsics, and taking questions.

Technologie

Writing high performance code
in NetCore 3.0

ORGANIZATION
PLATINUM SPONSORS
Thank you!
COLLABORATORS

@jcant0n
Javier is a Computer Science Engineer who has always had a passion for
3D graphics and software architecture. His professional achievements
include being MVP for Windows DirectX and DirectX XNA for the last
nine years, Xbox Ambassador, as well as Microsoft Student Partner and
Microsoft Most Valuable Student during his years at college. Currently
he works at Plainconcepts as Research Team Lead.
Javier Cantón
Research Team Lead

.NetCore 3.0 preview
Span<T> Memory<T>
ValueTuple
NetStandard 2.1
C# 8 Async Streams
Ref return

Micro-optimization are for
• BCL and Runtime
• Real Time applications
• Graphics development

1.000.000.000 / 24 hours
416.666.666 operations per hour

416.666.666 / 60 minutes
6.944.444 operations per minutes

6.944.444 / 60 seconds
115.740 operations per seconds

115.740 / 10 servers
11.5 ms per operation

Bottlenecks Rule
Pareto
20% of the code consume 80% of the resources
Pareto^2
4.0% of the code consume 64% of the resources
Pareto^3
0.8% of the code consume 51% of the resources

Issues
• GC, Garbage collector
• Pinned array

Value Type vs Reference Type
Stack Heap
Reference Type
Memory Location
Int var = 10
Value Type
Int var = 10

And our headerBytes?
Stack Heap
Reference Type Memory Location
10 72 12

And our headerBytes?
Stack Heap
HeaderBytes
10 72 12

Using stack memory
• Unsafe Nuget Library
“Provide a generic API to read from and write to a pointer”

OpenGL issue
• Queue<T>
• Interface and struct

Gen0
Gen1
.NET Managed Heap
Gen2
LOH
LOH = GEN 2 = FULL GC
Size >= 85.000 bytes

MathF
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00%
Pow
Exp
Tan
Atan
Atan2
Log
Asin
Log10
Sinh
Cosh
Sin
Ceiling
Acos
Floor
Cos
Sqrt

MathF
https://github.com/jcant0n/RayTracingInAWeekend/

Hardware Intrinsics
X1
Y1
Z1
W1
X2
Y2
Z2
W2
X
Y
Z
W
+
+
+
+
=
=
=
=
SIMD
X1
Y1
Y1
W1
X2
Y2
Z2
W2
+ =
X
Y
Y
W

Hardware Intrinsics
• System.Runtime.Intrinsics
• Vector64<T>
• Vector128<T>
• Vector256<T>
• System.Runtime.Intrinsics.X86
• Sse, Sse2,… Sse42
• Avx, Avx2
• Fma
• System.Runtime.Intrinsics.Arm.Arm64
• Simd

Thanks and …
See you soon!
Thanks also to the sponsors.
Without whom this would not have been posible.

Contenu connexe

Tendances

Users have several options for running graph algorithms with Apache Spark. To support a graph data architecture on top of its linear-oriented DataFrames, the Spark platform offers GraphFrames. However, due to the fact that GraphFrames are immutable and not a native graph, there are cases where it might not offer the features or performance needed for certain use cases. Another option is to connect Spark to a real-time, scalable and distributed native graph database such as TigerGraph. In this session, we compare three options — GraphX, Cypher for Apache Spark, and TigerGraph — for different types of workload requirements and data sizes, to help users select the right solution for their needs. We also look at the data transfer and loading time for TigerGraph.

Assessing Graph Solutions for Apache Spark

Databricks

Strata NYC 2015: Sketching Big Data with Spark: randomized algorithms for lar...

Databricks

The Dark Side Of Go -- Go runtime related problems in TiDB in production

PingCAP

Setting up InfluxData for IoT

InfluxData

FlinkML - Big data application meetup

Theodoros Vasiloudis

You can do cool and unexpected things if your entire type system is a first class citizen and accessible at runtime. With the introduction of spec, Clojure got its own distinct spin on a type system. Just as macros add another -time (runtime and compile time) where the full power of the language can be used, spec does to describing data. The result is an entire additional type system that is a first class citizen and accessible at runtime that facilitates validation, generative testing (a la QuickCheck), destructuring (pattern matching into deeply nested data), data macros (recursive transformations of data) and a pluginable error system. And then you can start building on top of it. The talk will be half introduction to spec and the ideas packed within it, and half experience report instrumenting 15k loc production codebase (primarily ETL and analytics) with spec.

Spec: a lisp-flavoured type system

Simon Belak

InfluxDB 1.0 - Optimizing InfluxDB by Sam Dillard

InfluxData

Engineering fast indexes

Daniel Lemire

Supercharge your Analytics with ClickHouse, v.2. By Vadim Tkachenko

Altinity Ltd

Google APAC Machine Learning Day 是 Google 今年三月初於新加坡 Google 辦公室針對機器學習所舉辦的兩天研討會活動，本次聚會將邀請前往參加該活動的 Evan Lin 及他的同事 Benjamin Chen 帶來他們的心得分享，內容包括: Tensorflow Summit RECAP Machine Learning Expert Day 所見所聞分享一下 Linker Networks 如何使用 Tensorflow https://gdg-taipei.kktix.cc/events/google-apac-machine-learning-day

Google APAC Machine Learning Expert Day

Evan Lin

Graphite

David Lutz

Clojure has always been good at manipulating data. With the release of spec and Onyx (“a masterless, cloud scale, fault tolerant, high performance distributed computation system”) good became best. In this talk you will learn about a data layer architecture build around Kafka and Onyx that is self-describing, declarative, scalable and convenient to work with for the end user. The focus will be on the power and elegance of describing data and computation with data; and the inferences and automations that can be built on top of that.

Spec + onyx

Simon Belak

Almost all organizations now have a need for datascience and as such the main challenge after determining the algorithm is to scale it up and make it operational. We at comcast use several tools and technologies such as Python, R, SaS, H2O and so on. In this talk we will show how many common use cases use the common algorithms like Logistic Regression, Random Forest, Decision Trees , Clustering, NLP etc. Spark has several Machine Learning algorithms built in and has excellent scalability. Hence we at comcast built a platform to provide DSaaS on top of Spark with REST API as a means of controlling and submitting jobs so as to abstract most users from the rigor of writing(repeating ) code instead focusing on the actual requirements. We will show how we solved some of the problems of establishing feature vectors, choosing algorithms and then deploying models into production. We will showcase our use of Scala, R and Python to implement models using language of choice yet deploying quickly into production on 500 node Spark clusters.

Using SparkML to Power a DSaaS (Data Science as a Service): Spark Summit East...

Spark Summit

This presentation walks through the process of building an image classifier using Keras with a TensorFlow backend. It will give a basic understanding of image classification and show the techniques used in industry to build image classifiers. The presentation will start with building a simple convolutional network, augmenting the data, using a pretrained network, and finally using transfer learning by modifying the last few layers of a pretrained network. The classification will be based on the classic example of classifying cats and dogs. The code for the presentation can be found at https://github.com/rajshah4/image_keras, and the presentation will discuss how to extend the code to your own pictures to make a custom image classifier.

Image Classification Done Simply using Keras and TensorFlow

Rajiv Shah

My Talk at GCPUG-Taiwan on 2015/5/8. You use BigQuery with SQL, but the internal work of BigQuery is very different from traditional Relational Database systems you may familiar with. One of the way to understand how BigQuery works is to see it from the cost you pay for BigQuery. Knowing how to save money while using BigQuery is to know how BigQuery works to some extent. In this session, let’s talk about practical knowledge (saving money) and exciting technology (how BigQuery works)!

You might be paying too much for BigQuery

Ryuji Tamagawa

@sbelak Simon Belak Using Onyx in anger Clojure has always been good at manipulating data. With the release of spec and Onyx ("masterless, cloud scale, fault tolerant, high performance distributed computation system") good became best. In this talk I will walk you through a data layer architecture build around Kafka an Onyx that is self-describing, declarative, scalable and convenient to work with for the end user. The focus will be on the power and elegance of describing data and computation with data; and the inferences and automations that can be built on top of that.

Using Onyx in anger

Simon Belak

Advanced kapacitor

InfluxData

OPTIMIZING THE TICK STACK

InfluxData

DOWNSAMPLING DATA

InfluxData

Time series are a sequence of data points positioned in order of time. Time series forecasting has two main purposes - to understand the mechanisms that lead to rise or fall, and to predict future values. Very often it analyses trends, cyclical events, seasonality and has unique importance in Economics and Business. The quality of predictions can be evaluated only in future due to temporal dependencies on previous data points and there are many model types for approximation. In this session we are going to talk about challenges, ways of improvement and technology stack like ML.NET, ARIMA, Python, Azure ML, Regression and FB Prophet

Forecasting time series powerful and simple

Ivo Andreev

Tendances (20)

Assessing Graph Solutions for Apache Spark

Strata NYC 2015: Sketching Big Data with Spark: randomized algorithms for lar...

The Dark Side Of Go -- Go runtime related problems in TiDB in production

Setting up InfluxData for IoT

FlinkML - Big data application meetup

Spec: a lisp-flavoured type system

InfluxDB 1.0 - Optimizing InfluxDB by Sam Dillard

Engineering fast indexes

Supercharge your Analytics with ClickHouse, v.2. By Vadim Tkachenko

Google APAC Machine Learning Expert Day

Graphite

Spec + onyx

Using SparkML to Power a DSaaS (Data Science as a Service): Spark Summit East...

Image Classification Done Simply using Keras and TensorFlow

You might be paying too much for BigQuery

Using Onyx in anger

Advanced kapacitor

OPTIMIZING THE TICK STACK

DOWNSAMPLING DATA

Forecasting time series powerful and simple

Similaire à DotNet 2019 | Javier Cantón - Writing high performance code in NetCore 3.0

Loom promises: be there!

Jean-Francois James

Machine Learning at the Limit John Canny, UC Berkeley How fast can machine learning and graph algorithms be? In "roofline" design, every kernel is driven toward the limits imposed by CPU, memory, network etc. This can lead to dramatic improvements: BIDMach is a toolkit for machine learning that uses rooflined design and GPUs to achieve two- to three-orders of magnitude improvements over other toolkits on single machines. These speedups are larger than have been reported for *cluster* systems (e.g. Spark/MLLib, Powergraph) running on hundreds of nodes, and BIDMach with a GPU outperforms these systems for most common machine learning tasks. For algorithms (e.g. graph algorithms) which do require cluster computing, we have developed a rooflined network primitive called "Kylix". We can show that Kylix approaches the rooline limits for sparse Allreduce, and empirically holds the record for distributed Pagerank. Beyond rooflining, we believe there are great opportunities from deep algorithm/hardware codesign. Gibbs Sampling (GS) is a very general tool for inference, but is typically much slower than alternatives. SAME (State Augmentation for Marginal Estimation) is a variation of GS which was developed for marginal parameter estimation. We show that it has high parallelism, and a fast GPU implementation. Using SAME, we developed a GS implementation of Latent Dirichlet Allocation whose running time is 100x faster than other samplers, and within 3x of the fastest symbolic methods. We are extending this approach to general graphical models, an area where there is currently a void of (practically) fast tools. It seems at least plausible that a general-purpose solution based on these techniques can closely approach the performance of custom algorithms. Bio John Canny is a professor in computer science at UC Berkeley. He is an ACM dissertation award winner and a Packard Fellow. He is currently a Data Science Senior Fellow in Berkeley's new Institute for Data Science and holds a INRIA (France) International Chair. Since 2002, he has been developing and deploying large-scale behavioral modeling systems. He designed and protyped production systems for Overstock.com, Yahoo, Ebay, Quantcast and Microsoft. He currently works on several applications of data mining for human learning (MOOCs and early language learning), health and well-being, and applications in the sciences.

SF Big Analytics & SF Machine Learning Meetup: Machine Learning at the Limit ...

Chester Chen

Backend.AI Technical Introduction (19.09 / 2019 Autumn)

Lablup Inc.

"Source Code Abstracts Classification Using CNN", Vadim Markovtsev, Lead Software Engineer - Machine Learning Team at Source {d} Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo Visit the conference website to learn more: www.datanatives.io Follow Data Natives: https://www.facebook.com/DataNatives https://twitter.com/DataNativesConf Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS About the Author: Currently Vadim is a Senior Machine Learning Engineer at source{d} where he works on deep neural networks that aim to understand all of the world's developers through their code. Vadim is one of the creators of the distributed deep learning platform Veles (https://velesnet.ml) while working at Samsung. Afterwards Vadim was responsible for the machine learning efforts to fight email spam at Mail.Ru. In the past Vadim was also a visiting associate professor at Moscow Institute of Physics and Technology, teaching about new technologies and conducting ACM-like internal coding competitions. Vadim is also a big fan of GitHub (vmarkovtsev) and HackerRank (markhor), as well as likes to write technical articles on a number of web sites.

"Source Code Abstracts Classification Using CNN", Vadim Markovtsev, Lead Soft...

Dataconomy Media

BigBench is the brand new standard for benchmarking and testing Big Data systems. This talk first introduces BigBench and how problems can it solve. Then, presents both Hive and Spark benchmark results with with their respective 1 and 2 versions under different configurations. Results are further classified by use cases, showing where each platform shines (or doesn't), and why, based on performance metrics and log-file analysis. The talk concludes with the main findings, the scalability and limits of each framework.

Using BigBench to compare Hive and Spark (short version)

Nicolas Poggi

In the analysis of big data there are problematic queries that don’t scale because they require huge compute resources and time to generate exact results. Examples include count distinct, quantiles, most frequent items, joins, matrix computations, and graph analysis. If approximate results are acceptable, there is a class of sub-linear, stochastic streaming algorithms, called "sketches", that can produce results orders-of magnitude faster and with mathematically proven error bounds. For interactive queries there may not be other viable alternatives, and in the case of extracting results for these problem queries in real-time, sketches are the only known solution. For any analysis system that requires these problematic queries from big data, sketches are a required toolkit that should be tightly integrated into the system's analysis capabilities. This technology has helped Yahoo successfully reduce data processing times from days to hours, or minutes to seconds on a number of its internal platforms. This talk covers the current state of our Open Source DataSketches.github.io library, which includes adaptations and example code for Pig, Hive, Spark and Druid and gives architectural examples of use and a case study. Speakers: Jon Malkin is a scientist at Yahoo working to extend the DataSketches library. His previous roles have involved large scale data processing for sponsored search, display advertising, user counting, ad targeting, and cross-device user identity modeling. Alexander Saydakov is a senior software engineer at Yahoo working on the open source Data Sketches project. In his previous roles he has been involved in building large-scale back-end data processing systems and frameworks for data analytics and experimentation based on Torque, Hadoop, Pig, Hive and Druid. Alexander’s education background is in the field of applied mathematics.

February 2017 HUG: Data Sketches: A required toolkit for Big Data Analytics

Yahoo Developer Network

Marcus Gründler talks about high performance software at the JAX Finance 2016 conference in London. Analyzing financial transactions data of a large number of portfolios and hundreds of millions transactions and quotes is a demanding job for any computing environment. Analysis systems don't necessarily have extremely low latency requirements such as trading systems do, but have to provide low user response times. Applying massive parallelism over CPU cores or even clusters of machines doesn't help much if you want to achieve response times of a few milliseconds. Unleashing the extreme power of todays CPUs with a stream oriented architecture - which is optimal for utilizing caches and branch prediction of modern CPUs - becomes the corner stone in such systems. The talk presents insights into a system that is based on the idea of multi version concurrency control (MVCC) using Apache Kafka and binary in-memory data representation. Learn how to achieve speed improvements of a factor of 100-200 times compared to classic object oriented design.

Financial Portfolio Management with Java on Steroids - JAX Finance 2016

aixigo AG

Title: Sista: Improving Cog’s JIT performance Speaker: Clément Béra Thu, August 21, 9:45am – 10:30am Video Part1 https://www.youtube.com/watch?v=X4E_FoLysJg Video Part2 https://www.youtube.com/watch?v=gZOk3qojoVE Description Abstract: Although recent improvements of the Cog VM performance made it one of the fastest available Smalltalk virtual machine, the overhead compared to optimized C code remains important. Efficient industrial object oriented virtual machine, such as Javascript V8's engine for Google Chrome and Oracle Java Hotspot can reach on many benchs the performance of optimized C code thanks to adaptive optimizations performed their JIT compilers. The VM becomes then cleverer, and after executing numerous times the same portion of codes, it stops the code execution, looks at what it is doing and recompiles critical portion of codes in code faster to run based on the current environment and previous executions. Bio: Clément Béra and Eliot Miranda has been working together on Cog's JIT performance for the last year. Clément Béra is a young engineer and has been working in the Pharo team for the past two years. Eliot Miranda is a Smalltalk VM expert who, among others, has implemented Cog's JIT and the Spur Memory Manager for Cog.

Sista: Improving Cog’s JIT performance

ESUG

To understand an application’s performance, first you have to know what to measure. That’s the easy part. How do you take those measurements? Store them? Analyze them? Get them to the people who need them? Well, that’s where things get complicated, especially in the high-traffic distributed systems of the modern web! Like careful scientists, we must observe our subjects without altering them, and we must report our findings quickly so that we have the data necessary to make smart choices about the health and growth of the system. Let’s explore the lessons learned by engineers at one of the world’s top web companies in their quest to find meaning at 5 MB/s. We’ll discuss the tools and techniques that enable the collection, indexing, and analysis of billions or more datapoints each hour, and learn how these same approaches can empower your applications and your business, no matter the scale.

Drinking from the Firehose - Real-time Metrics

Samantha Quiñones

Intel Parallel Studio XE 2016 網路開發工具包新版本功能介紹(現已上市,歡迎詢價)

Cheer Chain Enterprise Co., Ltd.

Производительность инференса - одна из самых серьезных проблем при внедрении DL приложений, так как она определяет, какое впечатление от сервиса останется у конечного пользователя, а также какова будет цена внедрения этого продукта. Таким образом, для инференса важно быть высокопроизводительным и энергоэффективным. TensorRT автоматически оптимизирует обученную нейронную сеть для максимальной производительности, обеспечивая существенное ускорение по сравнению с обычными часто используемыми фреймворками. Из презентации вы узнаете, какие оптимизации применяются в TensorRT, как его использовать и увидите, насколько он быстр в избранных задачах.

Высокопроизводительный инференс глубоких сетей на GPU с помощью TensorRT / Ма...

Ontico

Summit2013 eventos onto quad

Semantic Technology Institute International

Javantura v4 - Java and lambdas and streams - are they better than for loops ...

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

Summary of models and methods used for DAWNBench CIFAR-10 Challenge. Starting with an review of ResNets from high level architecture, we review Basic vs Bottleneck blocks, pre-activation blocks and Wide Resets. After a brief mention of PyramidNet, ResNext and DenseNet models, we look at regularization techniques such as Mixup. And we finish with a review of Cyclical Learning Rates, and the phenomenon of "Super Convergence". MXNet Gluon API was used for the implementations.

CIFAR-10 for DAWNBench: Wide ResNets, Mixup Augmentation and "Super Convergen...

Thom Lane

Chronix Poster for the Poster Session FAST 2017

Florian Lautenschlager

What’s easier than building a data pipeline nowadays? You add a few Apache Kafka clusters and a way to ingest data (probably over HTTP), design a way to route your data streams, add a few stream processors and consumers, integrate with a data warehouse… wait, this looks like a lot of things, doesn’t it? And you probably want to make it highly scalable and available too. Join this session to learn best practices for building a data pipeline, drawn from my experience at Activision/Demonware. I’ll share the lessons learned about scaling pipelines, not only in terms of volume, but also in terms of supporting more games and more use cases. You’ll also hear about message schemas and envelopes, Apache Kafka organization, topics naming conventions, routing, reliable and scalable producers and the ingestion layer, as well as stream processing.

Building Scalable and Extendable Data Pipeline for Call of Duty Games (Yarosl...

confluent

A Dataflow Processing Chip for Training Deep Neural Networks

inside-BigData.com

When you access a web page, bidders such as Criteo must determine in a few dozens of milliseconds if they want to purchase the advertising space on the page. At that moment, a real-time auction takes place, and once you remove all the communication exchange delays, it leaves a handful of milliseconds to compute exactly how much to bid. In the past year, Criteo has put a large amount of effort into reshaping its in-house machine learning stack responsible for making such predictions—in particular, opening it to new technologies such as TensorFlow. Unfortunately, even for simple logistic regression models and small neural networks, Criteo’s initial TensorFlow implementations saw inference time increase by 100, going from 300 microseconds to 30 milliseconds. Nicolas Kowalski and Axel Antoniotti outline how Criteo approached this issue, discussing how Criteo profiled its model to understand its bottleneck; why commonly shared solutions such as optimizing TensorFlow build for the target hardware, freezing and cleaning up the model, and using accelerated linear algebra (XLA) ended up being lackluster; and how Criteo rewrote is models from scratch, reimplementing cross-features and hashing functions using low-level TF operations in order to factorize as much as possible all TensorFlow nodes in its model.

How Criteo optimized and sped up its TensorFlow models by 10x and served them...

Nicolas Kowalski

In-memory processing has started to become the norm in large scale data handling. This is aclose to the metal analysis of highly important but often neglected aspects of memory accesstimes and how it impacts big data and NoSQL technologies.We cover aspects such as the TLB, the Transparent Huge Pages, the QPI Link, Hyperthreading and the impact of virtualization on high-memory footprint applications. We present benchmarks of various technologies ranging from Cloudera’s Impala to Couchbase and how they are impacted by the underlying hardware.The key takeaway is a better understanding of how to size a cluster, how to choose a cloud provider and an instance type for big data and NoSQL workloads and why not every core or GB of RAM is created equal.

Memory, Big Data, NoSQL and Virtualization

Bigstep

NVIDIA DGX User Group 1st Meet Up_30 Apr 2021.pdf

MuhammadAbdullah311866

Similaire à DotNet 2019 | Javier Cantón - Writing high performance code in NetCore 3.0 (20)

Loom promises: be there!

SF Big Analytics & SF Machine Learning Meetup: Machine Learning at the Limit ...

Backend.AI Technical Introduction (19.09 / 2019 Autumn)

"Source Code Abstracts Classification Using CNN", Vadim Markovtsev, Lead Soft...

Using BigBench to compare Hive and Spark (short version)

February 2017 HUG: Data Sketches: A required toolkit for Big Data Analytics

Financial Portfolio Management with Java on Steroids - JAX Finance 2016

Sista: Improving Cog’s JIT performance

Drinking from the Firehose - Real-time Metrics

Intel Parallel Studio XE 2016 網路開發工具包新版本功能介紹(現已上市,歡迎詢價)

Высокопроизводительный инференс глубоких сетей на GPU с помощью TensorRT / Ма...

Summit2013 eventos onto quad

Javantura v4 - Java and lambdas and streams - are they better than for loops ...

CIFAR-10 for DAWNBench: Wide ResNets, Mixup Augmentation and "Super Convergen...

Chronix Poster for the Poster Session FAST 2017

Building Scalable and Extendable Data Pipeline for Call of Duty Games (Yarosl...

A Dataflow Processing Chip for Training Deep Neural Networks

How Criteo optimized and sped up its TensorFlow models by 10x and served them...

Memory, Big Data, NoSQL and Virtualization

NVIDIA DGX User Group 1st Meet Up_30 Apr 2021.pdf

Plus de Plain Concepts

R y Python son lenguajes muy populares hoy en día especialmente para científicos de datos, que los utilizan para prospección, tratamiento y minería de datos y, Power BI es una de las herramientas que más está creciendo en cuanto a utilización y aceptación en el sector de inteligencia de negocios y análisis de datos. La sesión cubre, a través de demos, los puntos en los que ambos enfoques se combinan para sacar mejor partido a los datos con los que contamos. Según sea el caso, vamos a preferir gestionar nuestras tareas desde el mundo de estadísticas y gráficos ofrecido por lenguajes R y Python, el mundo más encaminado al análisis de negocio gestionado con Power BI, o ambos mundos.

R y Python con Power BI, la ciencia y el análisis de datos, juntos

Plain Concepts

¿Has recibido un e-mail con publicidad? ¿Muchos? ¿Te han suscrito a listas de distribución sin tu consentimiento? ¿Recibes preguntas insoportables? ¿Alguien te ha intentado hacer un phishing? ¿O te ha llamado un familiar porque se ha infectado con un ransomware que le entró por e-mail? ¿Ocultas como un poseso tu dirección de e-mail y número de teléfono para que no te machaquen? Bien, a mí también me ha pasado. Por eso hemos decidido que el e-mail debía sufrir una disrupción definitivamente.

Video kills the radio star: e-mail is crap and needed disruption

Plain Concepts

De la misma manera que la llegada del software ha transformado todo tipo de empresas e industrias a lo largo de los últimos 20 años, la Inteligencia Artificial está empezando a redefinir todo tipo de escenarios empresariales. Descubre en esta charla los conceptos básicos de la Inteligencia artificial y descubre los casos de uso más apropiados para tu tipo de empresa. Aprende a realizar el cambio organizacional y cultural necesario para potenciar tu negocio mediante IA.

Cómo redefinir tu organización con IA

Plain Concepts

Rare genetic diseases are very challenging to diagnose, with the average child waiting for diagnosis for 5 years. Next generation genetic sequencing data may hold the key to diagnosis, however analysis can become a paramount task with multiple factors affecting conclusions. Dx29, an AI-assisted platform facilitates this task, allowing the physician to drive the analysis. Dx29 is a free platform developed by Foudation29, in close collaboration with academic groups.

Dx29: assisting genetic disease diagnosis with physician-focused AI pipelines

Plain Concepts

Hoy en día es difícil no hablar de la Inteligencia Artificial y pensar en cómo se ha aplicado para resolver tareas difíciles y repetitivas para el ser humano. Pero en los últimos años, gracias a la llegada de las Redes Generativas Adversariales (GANs), la IA adoptó capacidades creativas que le permiten generar información artificial. Es la era de los Deepfakes, en la que puedes poner tu cara al actor de tu película favorita o ser felicitado por el presidente de los Estados Unidos. En esta charla, veremos gran parte de estas capacidades adquiridas por la IA, algunos ejemplos, y pondremos a prueba nuestro ojo para comprobar si estamos preparados para detectar que es real y que no.

¿Qué es real? Cuando la IA intenta engañar al ojo humano

Plain Concepts

El cáncer de mama es el tipo de cáncer más común entre mujeres y el segundo tipo de cáncer más común, siendo uno de las principales causas de muerte de las mujeres a nivel global. Sin embargo, la probabilidad de curación cuando éste es detectado en estados iniciales, es prácticamente del 100%. La Inteligencia Artificial (IA) puede ser una gran aliada para agilizar el diagnóstico reduciendo eficazmente tanto los tiempos de espera como el número de biopsias necesarias, aumentando de esta forma las probabilidades de curación. En esta charla veremos cómo. ¿Te lo vas a perder?

Inteligencia artificial para detectar el cáncer de mama

Plain Concepts

¿Conoces el impacto real que la IA está teniendo en las empresas y cuáles son los retos a los que se han enfrentado para implementarla con éxito? En esta charla veremos cómo la IA impacta en las diferentes industrias y el retorno de la inversión obtenido. También veremos cuáles son los principales retos a los que se han enfrentado las empresas para incorporar la IA como factor estratégico y las diferentes formas de abordarlos para obtener una implantación firme y estable que acelere el retorno de la inversión.

¿Está tu compañía preparada para el reto de la Inteligencia Artificial?

Plain Concepts

Gracias a los Cognitive Services ahora podemos añadir inteligencia a nuestras apps de una manera sencilla. La combinación de estos servicios abren un mundo nuevo de posibilidades, por lo que durante esta charla veremos una breve introducción a los distintos servicios para pasar directamente a verlos en acción en aplicaciones y situaciones reales. Se trata de una charla introductoria en la que haremos demos y veremos cómo podemos utilizar estos servicios en nuestro código.

Cognitive Services en acción

Plain Concepts

El Hogar Inteligente. De los datos de IoT a los hábitos de una familia a trav...

Plain Concepts

AI is the new buzzword, everybody is talking about it and how it will change and influence our lives. When we talk about AI we talk about machines learning from data, exactly like a child is learning from his/her family or the experiences he/she makes. Humans though, while they grow up, can develop biases. Could this happen to an AI too? Starting from a real story, what would happen if a machine learning algorithm learns from a toys catalog?

What if AI was your daughter?

Plain Concepts

En lo sitios web de eCommerce, la recomendación de productos es clave para poder exponer el catálogo completo al usuario. Una estrategia de recomendación sin datos de usuario es la llamada Recomendación basada en contenidos. En ésta se tienen en cuenta las características de los productos para buscar similitudes. En esta charla veremos diferentes formas de calcular la similitud de unos productos concretos, recetas, basadas en Deep Learning y cómo hemos implementado estos algoritmos en Azure. Finalmente, veremos qué problemas hemos detectado y cómo los estamos solucionando.

Recomendación Basada en Contenidos con Deep Learning: Qué queríamos hacer, Qu...

Plain Concepts

La experiencia de cliente continúa siendo el principal desafío al que se enfrentan las empresas en la era digital. La explosión del Big Data y la Inteligencia Artificial permite profundizar en el conocimiento del cliente y facilita la personalización de productos y servicios, estableciendo vínculos más fuertes y duraderos. A través de varios casos de uso reales, mostraremos cómo esta transformación está ya ocurriendo permitiendo a las empresas pioneras cambiar las reglas del juego en sus sectores.

Revolucionando la experiencia de cliente con Big Data e IA

Plain Concepts

IA Score en InfoJobs

Plain Concepts

Tratar de encontrar ofertas de trabajo que se ajusten a las habilidades de un buscador de empleo se ha convertido en un dolor de cabeza. La recuperación de información ha sido el método utilizado últimamente para ayudar en esta tarea. Con la inclusión de los algoritmos de aprendizaje profundo, la recuperación de información es ahora más poderosa que nunca. Permite el análisis de grandes conjuntos de documentos, haciendo que la predicción sea más precisa, incluso superando las capacidades humanas. En la presente ponencia presentamos las técnicas más avanzadas para la recuperación de información con un aprendizaje profundo y las aplicamos a la tarea de emparejar los currículums de los demandantes de empleo o a búsquedas específicas con las ofertas de empleo existentes más adecuadas.

Recuperación de información para solicitantes de empleo

Plain Concepts

¿Te has preguntado alguna vez que podríamos hacer con toda la telemetría que se recoge en fábricas y empresas? Durante esta sesión veremos como aplicar distintas técnicas de Inteligencia Artificial en el sector industrial para mejorar la seguridad y el rendimiento de nuestras instalaciones. Además, veremos una demo en vivo donde podremos observar como nuestro dispositivo IoT puede analizar los datos que recibe y ser capaz de predecir posibles fallos futuros en distintos componentes.

La nueva revolución Industrial: Inteligencia Artificial & IoT Edge

Plain Concepts

DotNet 2019 | Sherry List - Azure Cognitive Services with Native Script

Plain Concepts

DotNet 2019 | Quique Fernández - Potenciando VUE con TypeScript, Inversify, V...

Plain Concepts

DotNet 2019 | Daniela Solís y Manuel Rodrigo Cabello - IoT, una Raspberry Pi ...

Plain Concepts

El camino a las Cloud Native Apps - Introduction

Plain Concepts

El camino a las Cloud Native Apps - Azure AI

Plain Concepts

Plus de Plain Concepts (20)

R y Python con Power BI, la ciencia y el análisis de datos, juntos

Video kills the radio star: e-mail is crap and needed disruption

Cómo redefinir tu organización con IA

Dx29: assisting genetic disease diagnosis with physician-focused AI pipelines

¿Qué es real? Cuando la IA intenta engañar al ojo humano

Inteligencia artificial para detectar el cáncer de mama

¿Está tu compañía preparada para el reto de la Inteligencia Artificial?

Cognitive Services en acción

El Hogar Inteligente. De los datos de IoT a los hábitos de una familia a trav...

What if AI was your daughter?

Recomendación Basada en Contenidos con Deep Learning: Qué queríamos hacer, Qu...

Revolucionando la experiencia de cliente con Big Data e IA

IA Score en InfoJobs

Recuperación de información para solicitantes de empleo

La nueva revolución Industrial: Inteligencia Artificial & IoT Edge

DotNet 2019 | Sherry List - Azure Cognitive Services with Native Script

DotNet 2019 | Quique Fernández - Potenciando VUE con TypeScript, Inversify, V...

DotNet 2019 | Daniela Solís y Manuel Rodrigo Cabello - IoT, una Raspberry Pi ...

El camino a las Cloud Native Apps - Introduction

El camino a las Cloud Native Apps - Azure AI

Dernier

How to Troubleshoot Apps for the Modern Connected Worker

ThousandEyes

Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

sammart93

In this keynote, Asanka Abeysinghe, CTO,WSO2 will explore the shift towards platformless technology ecosystems and their importance in driving digital adaptability and innovation. We will discuss strategies for leveraging decentralized architectures and integrating diverse technologies, with a focus on building resilient, flexible, and future-ready IT infrastructures. We will also highlight WSO2's roadmap, emphasizing our commitment to supporting this transformative journey with our evolving product suite.

Platformless Horizons for Digital Adaptability

WSO2

Keynote 2: APIs in 2030: The Risk of Technological Sleepwalk Paolo Malinverno, Growth Advisor - The Business of Technology Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

apidays

Strategies for Landing an Oracle DBA Job as a Fresher

Remote DBA Services

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

Why Teams call analytics are critical to your entire business

panagenda

Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

DianaGray10

MS Copilot expands with MS Graph connectors

Nanddeep Nachan

Corporate and higher education. Two industries that, in the past, have had a clear divide with very little crossover. The difference in goals, learning styles and objectives paved the way for differing learning technologies platforms to evolve. Now, those stark lines are blurring as both sides are discovering they have content that’s relevant to the other. Join Tammy Rutherford as she walks through the pros and cons of corporate and higher ed collaborating. And the challenges of these different technology platforms working together for a brighter future.

Corporate and higher education May webinar.pptx

Rustici Software

Angeliki Cooney has spent over twenty years at the forefront of the life sciences industry, working out of Wynantskill, NY. She is highly regarded for her dedication to advancing the development and accessibility of innovative treatments for chronic diseases, rare disorders, and cancer. Her professional journey has centered on strategic consulting for biopharmaceutical companies, facilitating digital transformation, enhancing omnichannel engagement, and refining strategic commercial practices. Angeliki's innovative contributions include pioneering several software-as-a-service (SaaS) products for the life sciences sector, earning her three patents. As the Senior Vice President of Life Sciences at Avenga, Angeliki orchestrated the firm's strategic entry into the U.S. market. Avenga, a renowned digital engineering and consulting firm, partners with significant entities in the pharmaceutical and biotechnology fields. Her leadership was instrumental in expanding Avenga's client base and establishing its presence in the competitive U.S. market.

Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...

Angeliki Cooney

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

Zilliz

Whatsapp Number Escorts Call girls 8617370543 Available 24x7 Mcleodganj Call Girls Service Offer Genuine VIP Model Escorts Call Girls in Your Budget. Mcleodganj Call Girls Service Provide Real Call Girls Number. Make Your Sexual Pleasure Memorable with Our Mcleodganj Call Girls at Affordable Price. Top VIP Escorts Call Girls, High Profile Independent Escorts Call Girls, Housewife Women Escorts Call Girl, College Girls Escorts Call Girls, Russian Escorts Call girls Service in Your Budget.

Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model

Deepika Singh

The microservices honeymoon is over. When starting a new project or revamping a legacy monolith, teams started looking for alternatives to microservices. The Modular Monolith, or 'Modulith', is an architecture that reaps the benefits of (vertical) functional decoupling without the high costs associated with separate deployments. This talk will delve into the advantages and challenges of this progressive architecture, beginning with exploring the concept of a 'module', its internal structure, public API, and inter-module communication patterns. Supported by spring-modulith, the talk provides practical guidance on addressing the main challenges of a Modultith Architecture: finding and guarding module boundaries, data decoupling, and integration module-testing. You should not miss this talk if you are a software architect or tech lead seeking practical, scalable solutions. About the author With two decades of experience, Victor is a Java Champion working as a trainer for top companies in Europe. Five thousands developers in 120 companies attended his workshops, so he gets to debate every week the challenges that various projects struggle with. In return, Victor summarizes key points from these workshops in conference talks and online meetups for the European Software Crafters, the world’s largest developer community around architecture, refactoring, and testing. Discover how Victor can help you on victorrentea.ro : company training catalog, consultancy and YouTube playlists.

Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024

Victor Rentea

Elevate Developer Efficiency & build GenAI Application with Amazon Q

Bhuvaneswari Subramani

Boost Fertility New Invention Ups Success Rates.pdf

sudhanshuwaghmare1

Accelerating FinTech Innovation: Unleashing API Economy and GenAI Vasa Krishnan, Chief Technology Officer - FinResults Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

apidays

💉💊+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHABI}}+971581248768 +971581248768 Mtp-Kit (500MG) Prices » Dubai [(+971581248768**)] Abortion Pills For Sale In Dubai, UAE, Mifepristone and Misoprostol Tablets Available In Dubai, UAE CONTACT DR.Maya Whatsapp +971581248768 We Have Abortion Pills / Cytotec Tablets /Mifegest Kit Available in Dubai, Sharjah, Abudhabi, Ajman, Alain, Fujairah, Ras Al Khaimah, Umm Al Quwain, UAE, Buy cytotec in Dubai +971581248768''''Abortion Pills near me DUBAI | ABU DHABI|UAE. Price of Misoprostol, Cytotec” +971581248768' Dr.DEEM ''BUY ABORTION PILLS MIFEGEST KIT, MISOPROTONE, CYTOTEC PILLS IN DUBAI, ABU DHABI,UAE'' Contact me now via What's App…… abortion Pills Cytotec also available Oman Qatar Doha Saudi Arabia Bahrain Above all, Cytotec Abortion Pills are Available In Dubai / UAE, you will be very happy to do abortion in Dubai we are providing cytotec 200mg abortion pill in Dubai, UAE. Medication abortion offers an alternative to Surgical Abortion for women in the early weeks of pregnancy. We only offer abortion pills from 1 week-6 Months. We then advise you to use surgery if its beyond 6 months. Our Abu Dhabi, Ajman, Al Ain, Dubai, Fujairah, Ras Al Khaimah (RAK), Sharjah, Umm Al Quwain (UAQ) United Arab Emirates Abortion Clinic provides the safest and most advanced techniques for providing non-surgical, medical and surgical abortion methods for early through late second trimester, including the Abortion By Pill Procedure (RU 486, Mifeprex, Mifepristone, early options French Abortion Pill), Tamoxifen, Methotrexate and Cytotec (Misoprostol). The Abu Dhabi, United Arab Emirates Abortion Clinic performs Same Day Abortion Procedure using medications that are taken on the first day of the office visit and will cause the abortion to occur generally within 4 to 6 hours (as early as 30 minutes) for patients who are 3 to 12 weeks pregnant. When Mifepristone and Misoprostol are used, 50% of patients complete in 4 to 6 hours; 75% to 80% in 12 hours; and 90% in 24 hours. We use a regimen that allows for completion without the need for surgery 99% of the time. All advanced second trimester and late term pregnancies at our Tampa clinic (17 to 24 weeks or greater) can be completed within 24 hours or less 99% of the time without the need surgery. The procedure is completed with minimal to no complications. Our Women's Health Center located in Abu Dhabi, United Arab Emirates, uses the latest medications for medical abortions (RU-486, Mifeprex, Mifegyne, Mifepristone, early options French abortion pill), Methotrexate and Cytotec (Misoprostol). The safety standards of our Abu Dhabi, United Arab Emirates Abortion Doctors remain unparalleled. They consistently maintain the lowest complication rates throughout the nation. Our Physicians and staff are always available to answer questions and care for women in one of the most difficult times in their lives. The decision to have an abortion at the Abortion Cl

+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...

?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@

[BuildWithAI] Introduction to Gemini.pdf

Sandro Moreira

Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving

Edi Saputra

AWS Community Day CPH - Three problems of Terraform

Andrey Devyatkin

Dernier (20)

How to Troubleshoot Apps for the Modern Connected Worker

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

Platformless Horizons for Digital Adaptability

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

Strategies for Landing an Oracle DBA Job as a Fresher

Why Teams call analytics are critical to your entire business

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

MS Copilot expands with MS Graph connectors

Corporate and higher education May webinar.pptx

Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model

Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024

Elevate Developer Efficiency & build GenAI Application with Amazon Q

Boost Fertility New Invention Ups Success Rates.pdf

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...

[BuildWithAI] Introduction to Gemini.pdf

Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving

AWS Community Day CPH - Three problems of Terraform

DotNet 2019 | Javier Cantón - Writing high performance code in NetCore 3.0

1. Writing high performance code in NetCore 3.0

2. ORGANIZATION PLATINUM SPONSORS Thank you! COLLABORATORS

3. @jcant0n Javier is a Computer Science Engineer who has always had a passion for 3D graphics and software architecture. His professional achievements include being MVP for Windows DirectX and DirectX XNA for the last nine years, Xbox Ambassador, as well as Microsoft Student Partner and Microsoft Most Valuable Student during his years at college. Currently he works at Plainconcepts as Research Team Lead. Javier Cantón Research Team Lead

4. WaveEngine 3.0 preview

8. .NetCore 3.0 preview Span<T> Memory<T> ValueTuple NetStandard 2.1 C# 8 Async Streams Ref return

9. Micro-optimization are for • BCL and Runtime • Real Time applications • Graphics development

10. 1 billion operations per day

11. 1.000.000.000 / 24 hours 416.666.666 operations per hour

12. 416.666.666 / 60 minutes 6.944.444 operations per minutes

13. 6.944.444 / 60 seconds 115.740 operations per seconds

14. 115.740 / 10 servers 11.5 ms per operation

15. Bottlenecks Rule Pareto 20% of the code consume 80% of the resources Pareto^2 4.0% of the code consume 64% of the resources Pareto^3 0.8% of the code consume 51% of the resources

16. Real problem

17. KTX file loader

18. Header

23. Issues • GC, Garbage collector • Pinned array

24. Value Type vs Reference Type Stack Heap Reference Type Memory Location Int var = 10 Value Type Int var = 10

25. And our headerBytes? Stack Heap Reference Type Memory Location 10 72 12

26. And our headerBytes? Stack Heap HeaderBytes 10 72 12

27. Issues • GC, Garbage collector • Pinned array

28. Using stack memory • Stackalloc

29. Using stack memory • Unsafe Nuget Library “Provide a generic API to read from and write to a pointer”

30. Unsafe class

31. Unsafe class

32. Using stack memory

33. Real problem

34. Multithreading API

35. GPU Command Queue

36. OpenGL issue • Queue<T> • Interface and struct

37. Gen0 Gen1 .NET Managed Heap Gen2 LOH LOH = GEN 2 = FULL GC Size >= 85.000 bytes

38. Memory • System.Buffers • ArrayPool<T>

39. Custom collection

40. Custom collection

41. OpenGL (final solution)

42. Bonus Tanner Gooding

43. MathF New API for single-precisión math

44. MathF 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% Pow Exp Tan Atan Atan2 Log Asin Log10 Sinh Cosh Sin Ceiling Acos Floor Cos Sqrt

45. MathF https://github.com/jcant0n/RayTracingInAWeekend/

46. Hardware Intrinsics X1 Y1 Z1 W1 X2 Y2 Z2 W2 X Y Z W + + + + = = = = SIMD X1 Y1 Y1 W1 X2 Y2 Z2 W2 + = X Y Y W

47. Hardware Intrinsics • System.Runtime.Intrinsics • Vector64<T> • Vector128<T> • Vector256<T> • System.Runtime.Intrinsics.X86 • Sse, Sse2,… Sse42 • Avx, Avx2 • Fma • System.Runtime.Intrinsics.Arm.Arm64 • Simd

48. Hardware Intrinsics

49. Questions & Answers @jcant0n

50. Thanks and … See you soon! Thanks also to the sponsors. Without whom this would not have been posible.

Notes de l'éditeur

No que haya un pico sino mantener este ritmo de operaciones en el tiempo.
4.5 x faster.
Contains generic, low-level functionality for manipulating pointers.
Contains generic, low-level functionality for manipulating pointers.
Contains generic, low-level functionality for manipulating pointers.

DotNet 2019 | Javier Cantón - Writing high performance code in NetCore 3.0

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

Similaire à DotNet 2019 | Javier Cantón - Writing high performance code in NetCore 3.0

Similaire à DotNet 2019 | Javier Cantón - Writing high performance code in NetCore 3.0 (20)

Plus de Plain Concepts

Plus de Plain Concepts (20)

Dernier

Dernier (20)

DotNet 2019 | Javier Cantón - Writing high performance code in NetCore 3.0

Notes de l'éditeur