Jacket for MATLAB now available for NVIDIA Fermi!

ArrayFireAnnouncements 2 Comments

We are pleased to announce Jacket 1.4, with support for the latest NVIDIA graphics processing units based on the Fermi architecture (Tesla 20-series and GeForce GTX 4xx-series). NVIDIA’s release of the Fermi architecture brings with it 448 computational cores, increased IEEE-754 floating-point arithmetic precision, error-correcting memory for reliable computation, and enhanced memory caching mechanisms.

Highlights for Jacket 1.4 are as follows:

  • Added support for the NVIDIA Fermi architecture (GTX400 and Tesla C2000 series)
    • Jacket DLA support for Fermi
  • Dramatically improved the performance of Jacket’s JIT (Just-In-Time) compilation technology
    • Operations involving random scalar constants do not incur a recompile
    • Removed dependencies on MINGW and NVCC
  • Logical indexing now supported for SUBSREF and SUBSASGN, e.g. B = A(A > x)
  • MTIMES supports mixed types, no longer uses CUBLAS, and achieves better performance than CUBLAS
  • SUM, MIN, MAX, ANY, ALL now supported over any number of columns, rows, or dimensions
  • MIN, MAX indexed output now supported for complex single and complex double inputs
  • SUM, MIN, MAX over columns is greatly accelerated; vectors accelerated too
  • FIND performance improvements
  • CONVN, BLKDIAG, DOT performance improvements
  • CUMSUM now supported for matrices also
  • SORT, CONVN now supported in double-precision
  • HESS(A) and [P,H] = HESS(A) now supported (see Jacket DLA)
  • LEGENDRE now supported
  • Expanded GFOR support for:
    • FFT, FFT2, FFTN and inverses IFFT, IFFT2, IFFTN
  • PCG now supported, this is a system solver that uses the Preconditioned Conjugate Gradient Method for dense matrices
  • Image Processing Library now available. Direct access to the NVIDIA Performance Primitives (NPP) enabling new image processing functionality such as ERODE and DILATE.

The release notes are as follows:

See http://wiki.accelereyes.com/wiki/index.php/Release_Notes for full release notes.

Comments 2

  1. How do you evaluate the Performance of the new GTX460 in such situations?
    Does it have a substantial permanence advantage over the latest crop of CPU’s in Double Precision Operations?


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