How do you write code that interacts with the CPU cache?

1 What is the CPU cache?

The CPU cache is a hierarchical system of memory levels that are closer to the CPU than the main memory. The cache levels are usually denoted by L1, L2, L3, and so on, where L1 is the fastest and smallest, and L3 is the slowest and largest. The cache levels store copies of data blocks from the main memory that are likely to be used by the CPU soon. When the CPU needs to access a data block, it first checks the cache levels, starting from L1. If the data block is found in the cache, it is called a cache hit, and the CPU can access it quickly. If the data block is not found in the cache, it is called a cache miss, and the CPU has to fetch it from the main memory or a lower cache level, which is slower and more costly.

2 How does the CPU cache work?

The CPU cache works based on two principles: spatial locality and temporal locality. Spatial locality means that data blocks that are close to each other in the main memory are likely to be accessed together by the CPU. Temporal locality means that data blocks that are recently accessed by the CPU are likely to be accessed again soon. The CPU cache exploits these principles by storing data blocks in fixed-size units called cache lines, which are usually 64 bytes or 128 bytes. The cache lines are organized into sets, which are groups of cache lines that can store data blocks with the same index. The index is a part of the main memory address that determines which set a data block belongs to. The cache also uses a tag, which is another part of the main memory address that identifies a data block within a set. The cache uses a mapping function, such as direct mapping, associative mapping, or set-associative mapping, to decide which cache line in a set to store or replace a data block.

3 How can you measure the CPU cache performance?

One way to measure the CPU cache performance is to use the cache hit ratio, which is the percentage of memory accesses that result in cache hits. The higher the cache hit ratio, the better the CPU cache performance. Another way to measure the CPU cache performance is to use the cache miss penalty, which is the time delay caused by a cache miss. The lower the cache miss penalty, the better the CPU cache performance. You can use tools such as perf or cachegrind to monitor and analyze the CPU cache performance of your code.

4 How can you optimize the CPU cache usage?

Optimizing cache usage and reducing cache misses is a key goal when writing code that interacts with the CPU cache. To do this, you can align data structures to the cache line size, use contiguous memory allocation for arrays and vectors, access data in a sequential or strided pattern, reuse data whenever possible, and reduce the size and complexity of data structures. Doing these things can avoid splitting a data structure across multiple cache lines, improve the spatial locality of your data, match the cache line prefetching mechanism, exploit the temporal locality of your data, and fit more data into the cache.

5 How can you avoid the CPU cache interference?

Writing code that interacts with the CPU cache can be challenging due to the potential for cache interference, which is caused by multiple data blocks competing for the same cache line or set. This occurs on multi-core or multi-threaded CPUs, where each core or thread has its own cache or shares a cache with other cores or threads. To avoid this problem, you can use thread-local or core-local variables, pad or offset your data structures, use different cache levels for different data types, or use cache-aware or cache-oblivious algorithms. These strategies can prevent cache conflicts and minimize the number of cache misses.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?