SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC On-Demand

Algorithms
Presentation
Media
Welcome to the Jet Age – How AI and Deep Learning Make Online Shopping Smarter at Walmart
Daniel EGLOFF (QUANTALEA AG)
Online shopping is nothing if not efficient. Walmart together with new Jersey-startup Jet take things a step further, using AI and Deep Learning to optimize their entire E-Commerce business. The first AI application we discuss is Jet’s uni ...Read More

Online shopping is nothing if not efficient. Walmart together with new Jersey-startup Jet take things a step further, using AI and Deep Learning to optimize their entire E-Commerce business. The first AI application we discuss is Jet’s unique smart merchant selection: the platform finds the best merchant and warehouse combination in real time so that the total order cost is as low as possible. Then we show how to efficiently pack fresh and frozen orders with Deep Reinforcement Learning. The value of this approach is not just to find the best boxes and the tightest packing, but also the least amount of coolant and its placement so that the temperature of all items stays within the required limits during shipment.  

  Back
 
Keywords:
Algorithms, Other, High Performance Computing, GTC Europe 2017 - ID 23145
Download:
Deep Learning and AI
Presentation
Media
Prices Drop as You Shop: How Walmart is Using Jet's GPU based Smart Merchant Selection to Gain a Competitive Advantage
Daniel Egloff (QuantAlea and InCube)
Last year Walmart acquired the New Jersey based startup Jet to improve their e-commerce platform with new innovations, to compete more successfully in the e-commerce market and to optimize their order fulfillment costs. A core value of the Jet platfo ...Read More
Last year Walmart acquired the New Jersey based startup Jet to improve their e-commerce platform with new innovations, to compete more successfully in the e-commerce market and to optimize their order fulfillment costs. A core value of the Jet platform is the smart merchant selection. When a customer orders several items at once they usually can be fulfilled from multiple merchants and different warehouses. The goal is to find the merchant and warehouse combination so that the total order cost, including shipment costs and commissions, is as low as possible. Jet developed an innovative solution to find the most attractive combination of merchants. The bigger the shopping cart, the larger the savings that can be generated. We'll explain how only a clever combination of Machine Learning, new algorithms and GPUs at scale in the cloud can address the problem. This allows to unlock new use cases and business applications, which would not be possible with traditional computing resources.  Back
 
Keywords:
Deep Learning and AI, Performance Optimization, Finance, GTC 2017 - ID S7139
Download:
 
Alea TK - A new Deep Learning Stack for .NET
Daniel Egloff (QuantAlea AG)
Alea TK is a new open source library for general purpose numerical computing and Deep Learning based on tensors and tensor expressions supporting imperative calculations as well as symbolic calculations with auto-differentiation. It is designed ...Read More

Alea TK is a new open source library for general purpose numerical computing and Deep Learning based on tensors and tensor expressions supporting imperative calculations as well as symbolic calculations with auto-differentiation. It is designed from ground up with CUDA acceleration in mind. It is easy to extend, install and deploy and is perfectly suited for rapid prototyping and new model development. Alea TK is built entirely in .NET and C#. It relies on the Alea GPU compiler and uses NVIDIA's cuDNN library to accelerate many standard deep neural networks primitives.  Alea TK is still a young project. In this talk we explain the main design principles and present the framework to stimulate adoption by the community and show how to solve standard Deep Learning use cases. 

  Back
 
Keywords:
Deep Learning and AI, Tools and Libraries, GTC Europe 2016 - ID SEU6130
Developer - Programming Languages
Presentation
Media
Dynamic CUDA with F# - New Dimensions for GPU Computing on .NET
Daniel Egloff (QuantAlea GmbH), Xiang Zhang (QuantAlea GmbH)
CUDA and F# are two trailblazing yet unrelated technologies. F# is a uniquely productive language to solve complex problems in a clear and concise way. On the other hand CUDA is a platform for parallel high performance computing on GPUs. Our pre ...Read More

CUDA and F# are two trailblazing yet unrelated technologies. F# is a uniquely productive language to solve complex problems in a clear and concise way. On the other hand CUDA is a platform for parallel high performance computing on GPUs. Our presentation shows how to wed the two technologies F# and GPUs with the help of Alea. CUDA, a new framework and compiler infrastructure to develop GPU accelerated applications with F# on .NET. Alea.cuBase builds upon the LLVM compiler toolkit and the new NVIDIA PTX backend of CUDA 5. With Alea.cuBase you effectively write F# code, which generates CUDA programs dynamically at runtime, fully integrated into .NET. This approach opens up new dimensions for GPU programming, for example to develop non-trivial domain specific languages. Another interesting application is server side compilation of GPU kernels and GPU cloud computing. To give you a feeling of the new capabilities, we complement our presentation with several live coding examples.

  Back
 
Keywords:
Developer - Programming Languages, Developer - Tools & Libraries, Finance, GTC 2013 - ID S3055
Streaming:
Download:
 
Accelerating .NET Applications with Alea GPU
Daniel Egloff (QuantAlea)
Software companies use frameworks such as .NET to target multiple platforms from desktops to mobile phones with a single code base in order to reduce costs by leveraging existing libraries and to cope with changing trends. While developers can e ...Read More

Software companies use frameworks such as .NET to target multiple platforms from desktops to mobile phones with a single code base in order to reduce costs by leveraging existing libraries and to cope with changing trends. While developers can easily write scalable parallel code for multi-core CPUs on .NET, they face a bigger challenge using GPUs to tackle compute intensive tasks.  Alea GPU closes this gap by bringing GPU computing directly into the .NET ecosystem. In this hands-on webinar we show how you can write cross platform GPU accelerated .NET applications in any .NET language much easier than ever before. To follow the examples during the webinar, prepare your computer with Alea GPU and a free community license. Setup details can be found at http://bit.ly/1HL35a3. For more information, read Daniel's blog on Parallel Forall http://bit.ly/1Gu62Q3.

  Back
 
Keywords:
Developer - Programming Languages, GTC Webinars 2015 - ID GTCE115
Streaming:
Download:
Developer - Tools & Libraries
Presentation
Media
Alea.cuBase - A complete solution to develop CUDA accelerated applications on .NET
Daniel Egloff (QuantAlea GmbH)
CUDA and F# are two trailblazing yet unrelated technologies. F# is a uniquely productive language to solve complex problems in a clear and concise way. On the other hand CUDA is a platform for parallel high performance computing on GPUs. Our presenta ...Read More
CUDA and F# are two trailblazing yet unrelated technologies. F# is a uniquely productive language to solve complex problems in a clear and concise way. On the other hand CUDA is a platform for parallel high performance computing on GPUs. Our presentation shows how to wed the two technologies F# and GPUs with the help of Alea.CUDA, a new framework and compiler infrastructure to develop GPU accelerated applications with F# on .NET. Alea.CUDA builds upon the LLVM compiler toolkit and the new NVIDIA PTX backend of CUDA 5. With Alea.CUDA you effectively write F# code, which generates CUDA programs dynamically at runtime, fully integrated into .NET. This approach opens up new dimensions for GPU programming, for example to develop non-trivial domain specific languages. Another interesting application is server side compilation of GPU kernels and GPU cloud computing.   Back
 
Keywords:
Developer - Tools & Libraries, Developer - Programming Languages, GTC 2013 - ID P3107
Download:
 
Radically Simplified GPU Parallelization: The Alea Dataflow Programming Model
Luc Bläser (HSR University of Applied Sciences Rapperswil), Daniel Egloff (InCube Group, Quantalea)
Many programmers still leave the massive GPU parallel power unused be it because of lacking experience in CUDA or because of limited time and budget. We aim to drastically simplify GPU parallelization by introducing our Alea dataflow programming mo ...Read More
Many programmers still leave the massive GPU parallel power unused be it because of lacking experience in CUDA or because of limited time and budget. We aim to drastically simplify GPU parallelization by introducing our Alea dataflow programming model based on .NET. Complex computations can be easily and rapidly composed of a set of prefabricated and customizable operations that underlie asynchronous execution. The runtime system automatically translates this abstract model to efficient GPU code and schedules the operations with minimum memory transfers. By way of illustrative application cases of finance and statistics, we explain the model, take a look at the runtime system, and demonstrate its performance that proves to be as good as in manually optimized CUDA implementations.  Back
 
Keywords:
Developer - Tools & Libraries, Developer - Programming Languages, GTC 2015 - ID S5132
Streaming:
Download:
Finance
Presentation
Media
New Generation GPU Accelerated Financial Quant Libraries
Daniel Egloff (QuantAlea GmbH)
Learn from industry experts how new generation GPU accelerated solutions for derivative pricing, hedging, and risk management can be built more efficiently with modern technology and functional programming languages like F# on .NET or Scala on t ...Read More

Learn from industry experts how new generation GPU accelerated solutions for derivative pricing, hedging, and risk management can be built more efficiently with modern technology and functional programming languages like F# on .NET or Scala on the Java VM. As a concrete example we report from a large derivative pricing project developed in F# on .NET. We will introduce the key design concepts and parallelization strategies, which lead to an efficient and transparent GPU acceleration. Several examples will illustrate the benefit of the functional as compared to the classical object oriented approach.

  Back
 
Keywords:
Finance, GTC 2012 - ID S2405
Streaming:
Download:
 
GPUs in Quantitative Asset Management
Daniel Egloff (Incube Advisory and QuantAlea)
Modern portfolio theory, initially developed by Harry Markowitz, has been used in the industry for several decades to construct optimal portfolios, which properly balance risk and return. In recent years more refined quantitative methods have been de ...Read More
Modern portfolio theory, initially developed by Harry Markowitz, has been used in the industry for several decades to construct optimal portfolios, which properly balance risk and return. In recent years more refined quantitative methods have been developed to improve asset allocations and create optimal portfolios in a more stable and robust manner. We will discuss some of these new ideas and explain where large-scale numerical problems appear and how they can be solved with special algorithms on GPUs. You will learn how GPUs can help to consistently blend historical data and expert views in order to obtain more robust and realistic inputs for portfolio optimization, either with Bayesian techniques or with the minimum discrimination information principle, and how back-testing can be brought to a new level of sophistication.   Back
 
Keywords:
Finance, GTC 2014 - ID S4175
Streaming:
Download:
 
GPU Accelerated Backtesting and Machine Learning for Quant Trading Strategies
Daniel Egloff (InCube Group and QuantAlea)
In algorithmic trading large amounts of time series data are analyzed to derive buy and sell orders so that the strategy is profitable but also risk measures are at an acceptable level. Bootstrapping walk forward optimization is becoming increasingly ...Read More
In algorithmic trading large amounts of time series data are analyzed to derive buy and sell orders so that the strategy is profitable but also risk measures are at an acceptable level. Bootstrapping walk forward optimization is becoming increasingly popular to avoid curve fitting and data snooping. It is computationally extremely expensive but can be very well distributed to a GPU cluster. We present a framework for bootstrapping walk forward optimization of trading strategies on GPU clusters, which allows us to analyze strategies in minutes instead of days. Moreover, we show how signal generation can be combined with machine learning to make the strategies more adaptive to further improve the robustness and profitability.  Back
 
Keywords:
Finance, Machine Learning & Deep Learning, GTC 2015 - ID S5126
Streaming:
Download:
 
Going Deeper in Finance
Daniel Egloff (QuantAlea and InCube)
How wide is deep learning applicable in finance? We'll provide an overview of promising deep learning applications in finance. We'll then focus on deep (variational) autoencoders, showing how they can learn hidden representations of unlabeled data ...Read More
How wide is deep learning applicable in finance? We'll provide an overview of promising deep learning applications in finance. We'll then focus on deep (variational) autoencoders, showing how they can learn hidden representations of unlabeled data and generate new data. This opens interesting new applications in anomaly detection, risk analysis, price prediction, and algorithmic trading. We'll explore some of these use cases with real FX data and illustrate the concepts with interactive notebooks, showing how to build the models using frameworks such as Tensorflow and Keras, and how to use latest Tesla P100 GPUs for training.  Back
 
Keywords:
Finance, Deep Learning and AI, GTC 2017 - ID S7625
Download:
 
 
NVIDIA - World Leader in Visual Computing Technologies
Copyright © 2017 NVIDIA Corporation Legal Info | Privacy Policy