Deep Dive
Let's take a deeper look at some key technologies. Each article provides the important background you need (and links) to explore further.
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- Written by Douglas Eadline
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Explore time-series database processing
Analyzing time-series data for the financial markets is one case where speed matters. A faster answer means beating your competition to the market. For those who use kdb+/q, you understand this need and how choosing the right tool can make a huge difference in the speed at which queries get answered. As you may have assumed, there has always been an active community of kdb+ users, but until now there has never been a place to call your own.
That situation has changed thanks to kxcommunity.com. The new Kx Community site has been made available by Kx Systems, Inc. to assist and support kdb+ users. If you land on the main page, you are just a click away from current blogs, kdb+ events, job listings, local MeetUps, a monthly update signup, free code, and a real time twitter feed with kdb+ conversations. There is plenty more and the site has loads to offer for both new and experienced kdb+ users.
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- Written by Douglas Eadline
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A Deep Dive Exploring The Portland Group's PGI Accelerator Model
The remarkable computation power of General Purpose Graphical Processing Units (GP-GPUs) has led them to steadily gain traction in High Performance Computing (HPC). But creating GP-GPU programs can require new programming methods that often introduce additional work and code revisions, or even re-writes, and frequently become an obstacle to the adoption of GP-GPU technology.
To help solve this problem The Portland Group (PGI) has introduced an elegant way to augment existing HPC applications, allowing them to run efficiently on GP-GPUs while still maintaining their original code structure using standard Fortran or C. This approach provides a rapid and low-investment method for programmers to investigate GP-GPU computing. Recently, The Portland Group announced improved versions of their compiler and development tools that further enhance the PGI Accelerator Model for GP-GPUs.
