GTC 2013: Fuzzy Logix Launches Tanay Rx for GPU Accelerating Analytic Models Programmed In R
Subject: General Tech | March 26, 2013 - 11:40 PM | Tim Verry
Tagged: GTC 2013, gpu analytics, gpgpu, fuzzy logix
Fuzzy Logix, a company that specializes in HPC data analytics, recently unveiled a new extension (to the Tanay Zx library) called Tanay Rx that will GPU accelerate analytic models written in R. R is a programming language commonly used by statisticians. It is reportedly relatively easy to program, but has an inherent lack of multi-threading performance and memory limitations. With Tanay Rx, Fuzzy Logix is hoping to combine the performance benefits of its Tanay Zx libraries with the simplicity of R programming. According to Fuzzy Logix, Tanay Rx is "the perfect prescription to cure performance issues with R."
Tanay Zx allowed the use of many programming languages to run models with .net, .dll, or shared object calls on the GPU, and the new Tanay Rx extension extends that functionality to statistical and analytic models run using R. Models include those data intensive tasks as matrix operations, Monte Carlo simulations, data mining, financial mathematics (equities, fixed income, and time series analysis). Fuzzy Logix claims to enable R users to run over 500 analytic models up to 10 to 100-times faster by harnessing the parallel processing power of graphics and accelerator cards such as NVIDIA's Quadro/Tesla cards, Intel's MIC, and AMD's FirePro cards.
As an example, Fuzzy Logix states that calculations for intra-day risk of equity, interest rate, and FX options amount to approximately 1 billion future scenarios can be performed in milliseconds on the GPU. While some conversions may be more intensive, certain aspects of R code can be sped-up by replacing R functions with Fuzzy Logix' own Tanay Rx functions.
As per Fuzzy Logix's website.
Industry solutions implementing Tanay Rx for the financial, healthcare, internet marketing, pharmaceutical, oil, gas, insurance, and other sectors are available now. More information on the company's approach to GPGPU analytics is available here.