Jacket for MATLAB now available for NVIDIA Fermi!

by James Malcolm on July 14, 2010

in Announcements

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:
    • MLDIVIDE, INV, HESS, MTIMES
    • 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.

{ 2 comments }

Drazick July 14, 2010 at 11:11 pm

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?

Thanks.

John Melonakos July 19, 2010 at 4:54 pm

Hi Drazick,

Yes, the GTX460 performs really well in double-precision operations. We have seen a lot of speedups in applications against the latest CPUs. Speedups can depend on a lot of factors, see Wiki Documentation.

Best,

John

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