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There are two basic kinds of locality
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Spatial locality Memory that is physically close together should be used together For example, if an operation must access multiple memory locations, prefer to access those that will reside on the same cache line close together in the operation Typically this kind of locality is inherent in many programs If your program accesses one
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field of an object, the chances are very good that your program will need to access another field of that same object Larger cache lines prefetch data that is likely to be needed soon afterward
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Temporal locality Memory that must be used multiple times should be done as close (in time) as possible By doing so, the chance that the cache line on which the location resides will still be in the closest cache when subsequent operations are reached is greater
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Both are important Not programming in a locality conscious way will lead to an increase in CPI, which will slow your program down and increase memory bus traffic This can easily cause the memory system to become the bottleneck on parallel machines; ideally, the CPU would be the bottleneck, such that adding more processors will allow inherent scalabil ity to use them freely Programming in a locality conscious way is more of a heuristics based art than a well defined and verifiable methodology but is important to always keep in mind when designing data structures and algorithms for parallel programs
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The Cost of Sharing Let's see specifically why locality is important and what the effects of not paying attention to it can be When more than one processor shares access to a location in memory that resides on the same cache line, coherence traffic will increase and can negatively impact performance This is especially bad when the processors are performing writes, because it requires invalidation of lines in local processor caches This is particularly true of atomic (interlocked) operations because they must acquire cache lines in exclusive (E) mode Contention like this can even lead to an exclusive bus lock on older memory architectures What's worse, false sharing often leads to the symptoms of sharing, but is not always evident in the program This happens when two different memory locations are spatially collocated in memory, but logically distinct in the program For heap memory this is often a byproduct of how memory gets allocated In NET, the server GC has processor local heaps and so allo cations on separate processors should be physically separate enough to avoid this issue Similarly, many native memory allocators have processor local pools of free pages; this is primarily to avoid contention, but also helps
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avoid false sharing too Unfortunately, it's very easy to get into a situation where allocations happen together Another common situation in which false sharing crops up is when commonly read fields are close in memory to commonly written fields, usu ally on the same object A popular technique to reduce working set over head is called hot/cold splitting, which results in commonly used fields being collocated in memory together This is exactly the wrong thing to do, however, for parallel programs You want the commonly written fields as far away from the commonly read fields as possible This is important to keep in mind when designing new data structures Here is an example program that shows that a small mistake can make a large difference
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