Caching In On Program Performance

Most of us have a pretty simple model of how a computer works. The CPU fetches instructions and data from memory, executes them, and writes data back to memory. That model is a good enough abstraction for most of what we do, but it hasn’t really been true for a long time on anything but the simplest computers. A modern computer’s memory subsystem is much more complex and often is the key to unlocking real performance. [Pdziepak] has a great post about how to take practical advantage of modern caching to improve high-performance code.

If you go back to 1956, [Tom Kilburn’s] Atlas computer introduced virtual memory based on the work of a doctoral thesis by [Fritz-Rudolf Güntsch]. The idea is that a small amount of high-speed memory holds pieces of a larger memory device like a memory drum, tape, or disk. If a program accesses a piece of memory that is not in the high-speed memory, the system reads from the mass storage device, after possibly making room by writing some part of working memory back out to the mass storage device.

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Spectre And Meltdown: How Cache Works

The year so far has been filled with news of Spectre and Meltdown. These exploits take advantage of features like speculative execution, and memory access timing. What they have in common is the fact that all modern processors use cache to access memory faster. We’ve all heard of cache, but what exactly is it, and how does it allow our computers to run faster?

In the simplest terms, cache is a fast memory. Computers have two storage systems: primary storage (RAM) and secondary storage (Hard Disk, SSD). From the processor’s point of view, loading data or instructions from RAM is slow — the CPU has to wait and do nothing for 100 cycles or more while the data is loaded. Loading from disk is even slower; millions of cycles are wasted. Cache is a small amount of very fast memory which is used to hold commonly accessed data and instructions. This means the processor only has to wait for the cache to be loaded once. After that, the data is accessible with no waiting.

A common (though aging) analogy for cache uses books to represent data: If you needed a specific book to look up an important piece of information, you would first check the books on your desk (cache memory). If your book isn’t there, you’d then go to the books on your shelves (RAM). If that search turned up empty, you’d head over to the local library (Hard Drive) and check out the book. Once back home, you would keep the book on your desk for quick reference — not immediately return it to the library shelves. This is how cache reading works.

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