Telstra has revealed the addition of almost one million new mobile services in the six months to December 2011, but Sensis revenues plummeted 24 percent in 12 months.
Graphics chip maker Nvidia has launched a new high performance computing processor series based on its graphics processing unit (GPU) technology which the company claims will make parallel computing power more pervasive and affordable.
The Nvidia Tesla family of GPU computing
products span PCs to large scale server clusters and are aimed at the
physical sciences, geophysical and biosciences markets. Nvidia claims
that the Tesla family of GPU products will transorm workstations into
personal supercomputers.
Key elements of the new system are:
* A multi-threaded architecture with a 128-processor computing core
* A C-language development environment for the GPU
* A suite of developer tools (C-compiler, debugger, performance profiler, optimized libraries)
The Tesla family of products includes:
* Nvidia Tesla GPU Computing Processor, a dedicated computing board
that scales to multiple Tesla GPUs inside a single PC or workstation.
The Tesla GPU
features 128 parallel processors, and delivers up to 518 gigaflops of
parallel computation. The GPU Computing processor can be used in
existing systems partnered with high-performance CPUs.
* Nvidia Tesla Deskside Supercomputer, a scalable computing system
that includes two Nvidia Tesla GPUs and attaches to a PC or workstation
through an industry-standard PCI-Express connection. With multiple
deskside systems, a standard PC or workstation is transformed into a
personal supercomputer, delivering up to 8 teraflops of compute power
to the desktop.
* Nvidia Tesla GPU Computing Server, a 1U server housing up to
eight Nvidia Tesla GPUs, containing more than 1000 parallel processors
that add teraflops of parallel processing to clusters. The Tesla GPU
Server is the first server system of its kind to bring GPU computing to
the datacenter.
By comparison, the world's most powerful supercomputer, the IBM
BlueGene/L system at DOE’s Lawrence Livermore National Laboratory has
performance of 280.6 teraflops.
Software developers will be able to build high performance computing
applications using the Nvidia CUDA C development environment, which
includes a C-compiler for the GPU, debugger/profiler, dedicated driver,
and standard libraries. CUDA aims to simplify parallel computing
development by using standard C to create programs that process large
quantities of data in parallel. Nvidia CUDA is currently supported on
Linux and Windows XP.
Scientists and academics have appeared to be willing to lend their names in publicly supporting the new Nvidia platform.
"Many of the molecular structures we analyze are so large that they can
take weeks of processing time to run the calculations required for
their physical simulation," said John Stone, senior research programmer
at the University of Illinois Urbana-Champaign. "Nvidia's GPU computing
technology has given us a 100-fold increase in some of our programs,
and this is on desktop machines where previously we would have had to
run these calculations to a cluster."
David Bass
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