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Sandia simulations reveal memory is the bottleneck for some multi-core processors

Years ago, the hallmark of processor performance was clock speed. As chipmakers hit the wall on how far they could push clock speeds processor designs started to go to multiple cores to increase performance. However, as many users can tell you performance doesn't always increase the more cores you add to a system.

Benchmarkers know that a quad core processor often offers less performance than a similarly clocked dual-core processor for some uses. The reason for this phenomenon according to Sandia is one of memory availability. Supercomputers have tried to increase performance by moving to multiple core processors, just as the world of consumer processors has done.

The Sandia team has found that simply increasing the number of cores in a processor doesn't always improve performance, and at a point the performance actually decreases. Sandia simulations have shown that moving from dual core to four core processors offers a significant increase in performance. However, the team has found that moving from four cores to eight cores offers an insignificant performance gain. When you move from eight cores to 16 cores, the performance actually drops.

Sandia team members used simulations with algorithms for deriving knowledge form large data sets for their tests. The team found that when you moved to 16 cores the performance of the system was barely as good as the performance seen with dual-cores.

The problem according to the team is the lack of memory bandwidth along with fighting between the cores over the available memory bus of each processor. The team uses a supermarket analogy to better explain the problem. If two clerks check out your purchases, the process goes faster, add four clerks and things are even quicker.

However, if you add eight clerks or 16 clerks it becomes a problem to not only get your items to each clerk, but the clerks can get in each other's way leading to slower performance than using less clerks provides. Team member Arun Rodrigues said in a statement, "To some extent, it is pointing out the obvious — many of our applications have been memory-bandwidth-limited even on a single core. However, it is not an issue to which industry has a known solution, and the problem is often ignored."

James Peery, director of Sandia's Computations, Computers, Information, and Mathematics Center said, "The difficulty is contention among modules. The cores are all asking for memory through the same pipe. It's like having one, two, four, or eight people all talking to you at the same time, saying, 'I want this information.' Then they have to wait until the answer to their request comes back. This causes delays."

The researchers say that today there are memory systems available that offer dramatically improved memory performance over what was available a year ago, but the underlying fundamental memory problem remains.

Sandia and the ORNL are working together on a project that is intended to pave the way for exaflop supercomputing. The ORNL currently has the fastest supercomputer in the world, called the Jaguar, which was the first supercomputer to break the sustained petaflop barrier.



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By Fritzr on 1/18/2009 1:27:03 AM , Rating: 2
When scaling using clusters each node has it's own dedicated memory. The article is talking about multiple cores using a single memory which is what you get with the current multicore processors.

There is one memory connection reached through the memory controller and each core has to share that connection.

Assuming all cores are busy and reading/writing main memory then for a dual core the memory is half speed per core, quad core is quarter speed per core, eignt core is 1/8 speed per core ... as the number of cores goes up, the average available memory bandwidth per core drops.

One work around is larger unshared cache. The bigger the cache dedicated to each core the less that core is likely to need to go to main memory. As new code is written that is optimized in such a manner as to minimize main memory access the performance of multicore will go up.

For now when comparing multicore CPUs you need to look at per core dedicated cache. Larger cache boosts performance of multicore CPUs by reducing memory contention. This was the original solution used for supercomputers...each processor node has a large dedicated memory.


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