How does one write code that best utilizes the CPU cache to improve performance?

The cache is there to reduce the number of times the CPU would stall waiting for a memory request to be fulfilled (avoiding the memory latency), and as a second effect, possibly to reduce the overall amount of data that needs to be transfered (preserving memory bandwidth).

Techniques for avoiding suffering from memory fetch latency is typically the first thing to consider, and sometimes helps a long way. The limited memory bandwidth is also a limiting factor, particularly for multicores and multithreaded applications where many threads wants to use the memory bus. A different set of techniques help addressing the latter issue.

Improving spatial locality means that you ensure that each cache line is used in full once it has been mapped to a cache. When we have looked at various standard benchmarks, we have seen that a surprising large fraction of those fail to use 100% of the fetched cache lines before the cache lines are evicted.

Improving cache line utilization helps in three respects:

• It tends to fit more useful data in the cache, essentially increasing the effective cache size.
• It tends to fit more useful data in the same cache line, increasing the likelyhood that requested data can be found in the cache.
• It reduces the memory bandwidth requirements, as there will be fewer fetches.

Common techniques are:

• Use smaller data types
• Organize your data to avoid alignment holes (sorting your struct members by decreasing size is one way)
• Beware of the standard dynamic memory allocator, which may introduce holes and spread your data around in memory as it warms up.
• Make sure all adjacent data is actually used in the hot loops. Otherwise, consider breaking up data structures into hot and cold components, so that the hot loops use hot data.
• avoid algorithms and datastructures that exhibit irregular access patterns, and favor linear datastructures.

We should also note that there are other ways to hide memory latency than using caches.

Modern CPU:s often have one or more hardware prefetchers. They train on the misses in a cache and try to spot regularities. For instance, after a few misses to subsequent cache lines, the hw prefetcher will start fetching cache lines into the cache, anticipating the application's needs. If you have a regular access pattern, the hardware prefetcher is usually doing a very good job. And if your program doesn't display regular access patterns, you may improve things by adding prefetch instructions yourself.

Regrouping instructions in such a way that those that always miss in the cache occur close to each other, the CPU can sometimes overlap these fetches so that the application only sustain one latency hit (Memory level parallelism).

To reduce the overall memory bus pressure, you have to start addressing what is called temporal locality. This means that you have to reuse data while it still hasn't been evicted from the cache.

Merging loops that touch the same data (loop fusion), and employing rewriting techniques known as tiling or blocking all strive to avoid those extra memory fetches.

While there are some rules of thumb for this rewrite exercise, you typically have to carefully consider loop carried data dependencies, to ensure that you don't affect the semantics of the program.

These things are what really pays off in the multicore world, where you typically wont see much of throughput improvements after adding the second thread.

I can't believe there aren't more answers to this. Anyway, one classic example is to iterate a multidimensional array "inside out":

pseudocode
for (i = 0 to size)
  for (j = 0 to size)
    do something with ary[j][i]

The reason this is cache inefficient is because modern CPUs will load the cache line with "near" memory addresses from main memory when you access a single memory address. We are iterating through the "j" (outer) rows in the array in the inner loop, so for each trip through the inner loop, the cache line will cause to be flushed and loaded with a line of addresses that are near to the [j][i] entry. If this is changed to the equivalent:

for (i = 0 to size)
  for (j = 0 to size)
    do something with ary[i][j]

It will run much faster.