Taking advantage of multicore PCs
Call it the great multicore discord: a parade of major hardware and software vendors promising desktop applications powered by multicore chips yet all marching out of step, leaving confused software developers in the dust -- but times are changing.
Far out front, chipmakers Intel and AMD have delivered quad-core chips for desktop computers earlier this year. And computers with dual-core chips are now the norm. But only the savviest of developers can harness this massive processing power by weaving a mind-bending web of code that foundational software vendors should have provided. So much of the multicore chips' processing power is unharnessed.
Software vendors are finally closing the gap: Microsoft, Apple, third-party platform vendors, and software developer consortiums are tweaking everything from the operating system schedulers to APIs to languages and libraries to make them multicore-friendly. The goal, of course, is to make it easier for developers to join the multicore movement.
There's no question that the pace is quickening, the gap closing. Apple, for instance, claimed earlier this month that its upcoming Mac OS X Snow Leopard will boast a new technology, code-named Grand Central, that supports multicore chips, along with developer tools that let applications leverage up to eight cores of processing power.
To take advantage of multicore-enabling technologies such as Grand Central and whatever Microsoft may be working on for Windows 7 (the company declined to comment), developers must move up a steep learning curve in areas such as multithreading, parallel, and concurrent.
The first efforts to tap into multiple cores
Getting a desktop application today to truly take advantage of multicore chips is akin to Olympian gymnastics in that only a few elite developers are capable of performing at the level required. "The emphasis is on elite," says Dave Lounsbery, vice president of collaboration services at the Open Group, an industry group that promotes standards and industry-wide best practices. "This elite developer is able to go in and directly interact with the thread libraries and other things. He'd know that if he called the XYZ graphic function, it wasn't safe, and therefore, he'd be going from multicore to one core."
But most developers aren't Olympians, and so need development tools and operating systems to handle much of the multicore effort for them.
Software vendors are beginning to rise to the challenge, according to a Forrester Research report, by delivering low-level language extensions and libraries. For example, RapidMind offers a software development platform that allows developers to exploit quad-core AMD Opteron and Intel Xeon processors, which are typically used in servers. Graphics processor developer Nvidia provides its developers with a parallel programming language and libraries, called Cuda, to help developers access the graphical processor in a PC. But neither approach addresses the broad challenge: unlocking the multicore chips on desktop PCs and Macs for a broad range of applications.
However, some efforts are under way to make the multiple cores of today's
Build your tech library with our book giveaways.
Hacking Exposed, Sixth Edition
By Stuart McClure, Joel Scambray, George Kurtz; Published by McGraw-Hill/Osborne
The original Hacking Exposed authors rejoin forces on this tenth anniversary edition to offer completely up-to-date coverage of today's most devastating hacks and how to prevent them. Using their proven methodology, the authors reveal how to locate and patch system vulnerabilities. The book includes new coverage of ISO images, wireless and RFID attacks, Web 2.0 vulnerabilities, anonymous hacking tools, Ubuntu, Windows Server 2008, mobile devices, and more. Enter now!
Adding more multi-core
Adding more multi-core compute is not what we need.Current trends in microprocessor evolution indicate that more and more applications will become memory-bound and be unable to benefit from the ample compute resources offered by the next generation of computers.
This increasing disparity between a system’s compute capabilities and the available memory bandwidth needed to fully utilize that compute is the real problem.