Gigabyte GA-7PESH1 Review: A Dual Processor Motherboard through a Scientist’s Eyes

Simulations, Memory Requirements and Dual Processors

Throughout my simulation career, it would have been easy enough to just write code, compile and simply watch it run.  But the enthusiast and speed freak within me wanted the code to go a little faster, then a little faster, until learning about types of memory and how to prioritize code became part of my standard code scenario.  There are multiple issues that all come together from all sides of the equation.

First of all, let us discuss at a high level the concept of memory caching on a pseudo-processor. 

The picture above is a loose representation of a dual core processor, with each core represented as ‘P’, Registers labeled as ‘R’, and the size of the lines is representative of the bandwidth.

The processor has access to some registers which are a high-bandwidth, low space memory store.  These registers are used to store intermediary calculation data, as well as context switching with HyperThreading.  The processor is also directly linked to an L1 (‘level 1’) cache, which is the first place the processor looks if it needs data from memory.  If the data is not in the L1 then it looks in the L2 (‘level 2’), and so on until the data is found.  Obviously the closer the data is to the processor, the quicker it can be accessed and the calculation should prove to be quicker, and thus there are large benefits to larger caches. 

In the diagram above, each processor core has its own L1 cache and L2 cache, but a shared L3 cache.  This allows each core to probe the data in L3.  What is not shown is that there are some snoop protocols designed to let each core know what is going on in another core’s L2 cache.  With data flying around it is most important to maintain cache coherency.

Take an example where we have a simulation running two threads on our imaginary processor, and each thread requires 200 kB of data.  If our L2 cache is 256 kB then the thread can easily run inside the L2 keeping data rates high.  In the event that each core needs data from the other thread, values are copied into L3 at the expense of time.

Now imagine that our processor supports HyperThreading.  This allows us to run two threads on each processor core.  We still have the same amount of hardware, but when one thread is performing a memory read or write operation, that creates a delay until the read or write operation is confirmed.  While this delay is occurring, the processor core can save the state of the first thread and move on with the second.

The downside to our new HyperThreading scenario is when we launch a program with four threads, and each thread uses 200 kB of memory.  If our L2 cache is only 256 kB, then the combined 400 kB of data spills over into our L3 cache.  This has the potential of slowing down simulations if read and write operations are very slow.  (In modern processors, a lot of the logic built into the processor is designed to move data around such that these memory operations are as quick as possible – it goes ahead and predicts which data is needed next.)

This is the simple case of a dual core processor with HyperThreading.  It gets even more complicated if you add in the concept of dual processors.

If we have two dual core processors (four cores total) with HyperThreading (eight threads), the only memory share between the processors is the main random access memory.  When a standard program launches multiple threads, there is no say in where those threads will end up – they may be run out-of-order on whatever processor core is available.  Thus if one thread needs data from another, several things may occur:

(1) The thread may be delayed until the other thread is processed
(2) The data may already be on the same processor
(3) The data may be on the other processor, which causes delays

There are many different types of simulation that can be performed, each with their own unique way of requesting memory or dealing with threads.  As mentioned in the first page of this review, even in the research group I was in, if two people wrote code to perform the same simulation, memory requirements of each could be vastly different.  This makes it even more complicated, as when moving into a multithreaded scenario the initially slower simulation might be sped up the most.

Talking About Simulations

The next few pages will talk about a different type of simulation in turn based on my own experiences and what I have coded up.  Several are based on finite-difference grid solvers (both explicit and implicit), we have a Brownian test based on six movement algorithms, an n-body simulation, and our usual compression / video editing tests.  The ones we have written for this review will be explained briefly both mathematically and in code.