If you want to understand where we are going with computer architectures and the compilers that drive them, it is instructive to look at how compilers have made the leap from architecture to architecture starting six decades ago. …
On the hardware side, the next frontier for deep learning innovation will be in getting the performance, efficiency, and accuracy needed for inference at scale. …
A high-end supercomputer consumes nearly as much energy as a small city, which creates significant budgetary concerns for organizations that deploy and manage these systems. …
A working group formed on behalf of the Exascale Computing Project (ECP) in the U.S. …
Enterprises that want to leverage the huge amounts of data they are generating to gain useful insights and make faster and better business decisions are going to have to use machine learning at scale for modeling and training. …
As the rubber begins to meet the road for quantum computing, the conversation is shifting from one about the practicality of hardware to how future users will interface with quantum systems. …
When it comes to building extreme scale computing platforms, there are plenty of system design options but in supercomputing, the only practical choice for an OS is Linux. …
There is no way to predict which quantum system will garner system share in the next years, but most large chip, system, and software companies are hedging their bets and establishing a quantum research effort in the form of hardware, simulations, or software stacks. …
No matter what language or techniques are being applied, there are enough similarities between data science approaches that some broad parallels can be drawn. …
HPC luminary Jack Dongarra (University Distinguished Professor University of Tennessee) presented a new direction for math libraries at the International Supercomputing conference (ISC) in Frankfurt Germany in his presentation “Numerical Linear Algebra for Future Extreme-Scale Systems (NLAFET)”. …
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