Alea GPU 2.0 Final Release

In Feburary 2015 we made the release candidate of Alea GPU 2.0 available.

Just in time for GTC 2015 we released the final Alea GPU on nuget.org.

Alea GPU is a professional cross-platform GPU development environment for .NET.

  • Supports all .NET languages, including C#, F# and VB
  • Improves developer productivity with first class tooling for coding, debugging and profiling, fully integrated in Visual Studio™
  • Reduces development time with pre-fabricated GPU algorithms and libraries
  • Increases agility with GPU scripting and a REPL in Visual Studio™ for rapid prototyping of GPU code

With Alea GPU the .NET framework can be used as a cross platform technology for the CPU and GPU code base.

  • Runs on Windows, Linux and Mac OS X
  • Single code base for multiple platforms – build once and run on any platform supporting either .NET or Mono
  • Simplified deployment because generated assemblies are binary compatible for all platforms

Radically Simplified GPU Parallelization: The Alea Dataflow Programming Model

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 run-time 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 run-time system, and demonstrate its performance that proves to be as good as in manually optimized CUDA implementations.

Presentation slides


GPU Accelerated Backtesting and ML for Quant Trading Strategies

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.

Presentation slides