Introducing

MtxVec v5

Multicore math engine for science and engineering

Develop with Delphi, C# or C++ and deliver the code speed of assembler.

Comprehensive and fast numerical math library

Support for VS.NET, Embarcadero Delphi and C++ Builder

Statistical and DSP add-ons

MtxVec

Multicore math engine for science and engineering

DSP Master

Advanced signal processing package

Stats Master

Statistical package

Data Miner

Artificial intelligence enabling components

FFT Properties

Signal Analyzer and Recorder

Latest News

Desktop CPU Performance progress from 2013 until 2017

Written on Wednesday, 11 April 2018

We took an Intel Core i7-7820X for a spin and compared the speed-up for scientific computations to Intel Core i5-4670. In the table below you can see some results, which are very typical across a large range of different scientific algorithms. The test run is from our "Efficient multithreading" example in the MtxVec demo. The code computes DFT using vectorized sin, cos, add, multiply and sum of vector.

	i5-4670, 3.40 GHz	i7-7820X, 3.60 GHz
Pure pascal, one CPU core (not vectorized)	40.24s	34.59s
MtxVec, one CPU core (vectorized)	7.12s	5.86s
MtxVec with blocks, one CPU core	6.80s	4.67s
MtxVec with hand-written blocks	5.75s	4.25s
MtxVec threaded (naive)	9.12s	7.22s
MtxVec (threaded, with blocks)	1.77s	1.22s
MtxVec (threaded, blocks, DoForLoop, Annonymous method)	1.78s	1.18s
MtxVec (threaded, hand written blocks, DoForLoop)	1.54s	1.11s
MtxVec (threaded, blocks, TParallel.For)	2.93s	2.27s

The code executed with MtxVec takes full advantage of all instruction set features. This includes AVX-512 included with i7 7820X. Both CPUs were using 4 cores only. Typical improvement across all variants is around 30%. Note that "turbo" frequencies between both CPUs are different. When using AVX, the CPU will also not "turbo boost" up to the highest frequency. i7-7820X was mostly boosting up to 4.0GHz and the i5-4670 remained at 3.4GHz. The test was run with "default" optimized motherboard configuration and without overclocking.

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Numerical library for Delphi and .NET developers

Dew Research develops mathematical software for advanced scientific computing trusted by many customers. MtxVec for Delphi, C++ Builder or .NET is alternative for products like Matlab, LabView, OMatrix, SciLab, etc. We offer a high performance math library, statistics library and digital signal processing library (dsp library) for:

Embarcadero/CodeGear Delphi and C++Builder numerical libraries and components
Microsoft .NET components -- including Visual Studio add-ons and a .NET numerical library for C++, C#, and Visual Basic

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Product Features

MtxVec is an object oriented vectorized math library, the core of Dew Lab Studio, featuring a comprehensive set of mathematical and statistical functions executing at impressive speeds.

Designed for large data sets with complete vector/matrix arithmetic, it adds the following capabilities to your development environment:

A comprehensive set of mathematical, signal processing and statistical functions
Substantial performance improvements of floating point math by exploiting the SSE2, SSE3, SSE4.2 and Intel AVX 1.0 and AVX 2.0 instruction sets offered by modern CPUs.
Solutions based on it scale linearly with core count which makes it ideal for massively parallel systems.
Improved compactness and readability of code.

Support for native 64bit execution gives free way to memory hungry applications
Significantly shorter development times by protecting the developer from a wide range of possible errors.
Direct integration with TeeChart© to simplify and speed up the charting.
No royalty fees for distribution of compiled binaries in products you develop

Displaying large amounts of data

Superconductive memory manager

Linear and cubic interpolation

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Optimized Functions

The base math library uses the LAPACK (Linear Algebra Pack) version optimized for Core Duo and Core i7 CPU’s provided by Intel with their Math Kernel library. Our library is organized into a set of “primitive” highly optimized functions covering all the basic math operations. All higher level algorithms use these basic optimized functions, similar to the way LAPACK uses the Basic Linear Algebra Subprograms (BLAS).

Learn more: MtxVec encapsulates LAPACK

Performance Secrets

Code vectorization

The program achieves substantial performance improvements in floating point arithmetic by exploiting the CPU Streaming SIMD Extensions (SSE) 2, 3 and SSE4 instruction sets. (SIMD = Single Instruction Multiple Data.)

Learn more: Getting up to speed

Super conductive memory management

Effective massively parallel execution is achieved with the help of a super conductive memory management, which features zero thread contention and inter-lock problems allowing linear scaling with number of cores while maintaining low memory consumption and no interference with non-computational parts of the project.

Learn more: Super conductive memory manager

Some of our customers

Bank for International Settlements (BIS)

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