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Announcing NMath 6.1 and NMath Stats 4.1

Thursday, April 30th, 2015

We’re pleased to announce new versions of the NMath libraries – NMath 6.1, and NMath Stats 4.1.

Added functionality includes:

  • Upgraded to Intel MKL 11.2 Update 2 with resulting performance increases.
  • Updated NMath Premium GPU code to CUDA 6.
  • Added classes for solving linear and nonlinear programming problems with integer or binary constraints.
  • Added class SpecialFunctions containing special functions such as factorial, binomial, the gamma function and related functions, Bessel functions, elliptic integrals, and many more. (Prior versions of a few of these functions, such as StatsFunctions.IncompleteGamma, are now deprecated.)
  • Added a new native library, nmath_sf_x86.dll and nmath_sf_x64.dll, with high-performance C language implementations of the special functions.
  • Added single-precision versions of general sparse matrix and vector types.

For more complete changelogs, see here and here.

Upgrades are provided free of charge to customers with current annual maintenance contracts. To request an upgrade, please visit our upgrade page, or contact sales@centerspace.net. Maintenance contracts are available through our webstore.

Announcing NMath 6.0 and NMath Stats 4.0

Tuesday, August 19th, 2014

We’re pleased to announce new versions of the NMath libraries – NMath 6.0, and NMath Stats 4.0.

Added functionality includes:

  • Upgraded to Intel MKL 11.1 Update 3 with resulting performance increases.
  • Added Adaptive Bridge™ technology to NMath Premium edition, with support for multiple GPUs, per-thread control for binding threads to GPUs, and automatic performance tuning of individual CPU–GPU routing to insure optimal hardware usage.
  • NMath linear programming, nonlinear programming, and quadratic programming classes are now built on the Microsoft Solver Foundation (MSF). The Standard Edition of MSF is included with NMath.
  • Added classes for solving nonlinear programming problems using the Stochastic Hill Climbing algorithm, for solving quadratic programming problems using an interior point algorithm, and for solving constrained least squares problems using quadratic programming methods.
  • Added support for MKL Conditional Numerical Reproducibility (CNR).

For more complete changelogs, see here and here.

Upgrades are provided free of charge to customers with current annual maintenance contracts. To request an upgrade, please contact sales@centerspace.net. Maintenance contracts are available through our webstore.

CenterSpace in Chicago and Singapore

Wednesday, June 18th, 2014

NVIDIA GPU Technology Workshop in SE Asia

CenterSpace will be giving a presentation at the upcoming GPU Technology Workshop South East Asia on July 10. The conference will be held at the Suntec Singapore Convention & Exhibition Centre. For a full schedule of talks see the agenda.

Abstract

From CPU to GPU: a comparative case study / Andy Gray – CenterSpace Software

In this code-centric presentation, we will compare and contrast several approaches to a simple algorithmic problem: a straightforward implementation using managed code, a multi-CPU approach using a parallelization library, coupling object-oriented managed abstractions with high-performance native code, and seamlessly leveraging the power of a GPU for massive parallelization without code changes.

Andy Gray, a technology evangelist for CenterSpace Software, will be delivering the talk. We hope to see you there!

Parallel Computing in Finance Lecture

The June 5-6 conference at the University of Chicago titled, Recent Developments in Parallel Computing in Finance hosted talks by various academics in finance, Microsoft, Intel, and CenterSpace. CenterSpace was invited to give a two hour lecture and tutorial on GPU computing at the Stevanovich Center at the University of Chicago. We will post up the tutorial video from the talk as soon as it becomes available.

Abstract

Lecture by Trevor Misfeldt

CenterSpace Software, a leading provider of numerical component libraries for the .NET platform, will give an overview of their NMath math and statistics libraries and how they are being used in industry. The Premium Edition of NMath offers GPU parallelization. Xeon Phi, C++ AMP and CUDA are technologies of interest. Support for each will be discussed. Also discussed will be CenterSpace’s Adapative Bridge™ technology, which provides intelligent, adaptive routing of computations between CPU and GPUs. The presentation will finish with a demonstration followed by performance charts.

Tutorial by Andy Gray

In this hands-on programming tutorial, we will compare and contrast several approaches to a simple algorithmic problem: a straightforward implementation using managed code, a multi-CPU approach using a parallelization library, coupling object-oriented managed abstractions with high-performance native code, and seamlessly leveraging the power of a GPU for massive parallelization.

Fast Arction charts with NMath

Tuesday, March 15th, 2011

Our partners at Arction have created some great video showcasing their very fast signal processing charts using NMath.

More at Arction including information on our partner bundle.

– Trevor

NMath and Silverlight

Tuesday, March 1st, 2011

Customers have asked about using NMath from a Silverlight application.

NMath uses unmanaged code–specifically, Intel’s Math Kernel Library (MKL), an implementation of the BLAS and LAPACK standard.

In Silverlight 4.0, you can use NMath in two ways. You can either use JavaScript to talk to the server and have NMath running there, or you can register classes as COM objects on the local machine and call into them.

In Silverlight 5.0, you will be able to call unmanaged code directly on the client. NMath will have to exist in the client, but interfacing with it should be quite simple.

– Trevor