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CuLab - GPU Accelerated by Ngene - Toolkit for LabVIEW Download

CuLab - GPU Accelerated Toolkit

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ReleasedFeb 08, 2023
Publisher Ngene
License Ngene Custom
LabVIEW VersionLabVIEW x64>=20.0
Operating System Windows
Project links Homepage   Documentation   Repository  


CuLab is a very intuitive and simple to use toolkit for LabVIEW designed to accelerate computationally intensive tasks on Nvidia GPUs.

The purpose of CuLab is to provide extensive API functions to accelerate mathematical operations, BLAS (Basic Linear Algebra Subroutine) functions and common signal processing functions (FFT/IFFT) on GPUs.
Almost all CuLab operation functions support all numeric data types.

The main idea of the toolkit is to provide simple mechanisms to accelerate any data processing code developed in LabVIEW on Nvidia GPUs.

Release Notes (Feb 08, 2023)


General Description
This version of CuLab toolkit brings new functionalities and improves the performance of existing ones.

Backward Compatibility
This is a major update which breaks backward compatibility with v1.0.1 version of the toolkit.

1. Added Trigonometric functions:
a) Sine
b) Cosine
c) Tangent
d) Cotangent
e) Inverse Sine
f) Inverse Cosine
g) Inverse Tangent
h) Inverse Tangent 2 Input (atan2)
i) Inverse Cotangent
j) Sine & Cosine
k) Sinc

2. Added Exponential functions
a) Exponential
b) Exponential Arg -1
c) Logarithm Base 10
d) Logarithm Base 2
e) Logarithm Base X
f) Natural Logarithm
g) Natural Logarithm Arg +1
h) Power Of 10
i) Power Of 2
j) Power Of X
k) Y-th Root of X

3. Added Hyperbolic functions
a) Hyperbolic Sine
b) Hyperbolic Cosine
c) Hyperbolic Tangent
d) Hyperbolic Cotangent
e) Inverse Hyperbolic Sine
f) Inverse Hyperbolic Cosine
g) Inverse Hyperbolic Tangent
h) Inverse Hyperbolic Cotangent

4. Added missed complex APIs
a) Complex to Polar
b) Polar to Complex
c) Polar to Re/Im
d) Re/Im to Polar

5. Added function for Quotient & Remainder
6. Added support for Tensor-Constant operations for binary numeric operations
7. This allows to choose a CPU based constant as a second operand
8. Added new function generation functions
a) Ramp Pattern
b) Sine Pattern
c) Power Spectrum

9. Added support for missing numeric types for numeric operations
10. Redesigned colors for tensor wires to make them distinguishable across numeric types and dimensionality
11. Added batched versions of FFT and IFFT
12. Added spectrum shifting functionality single channel and batched FFT

1. Greatly improved the performance of Real-to-Complex (R2C and D2Z) FFTs
2. Optimized execution times for Complex functions
3. Other optimizations

Bug Fix
1. Fixed automatic instance selection issue in Array Max Min polymorphic function
2. Fixed memory leakage issue
3. Fixed an error in BLAS benchmarking example
4. Corrected typos
5. Other bug fixes

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