Linked lists are celebrity data structures of software development. They are celebrities because every engineer has had something to do with them in one part of their career. They are used in many places: from low-level memory management in operating systems up to data wrangling and data filtering in machine learning. They promise a lot:…
We use matrix multiplication example to investigate loop interchange and loop tiling as techniques to speed up your program that works with matrices.
We talk about virtual functions, and how the performance of software with virtual functions depends on many factors: the cost of additional instructions, cache misses, branch prediction misses, instruction cache misses and compiler optimizations.
We are exploring how class size and layout of its data members affect your program’s speed
Big O notation is commonly used to describe algorithm performance. But modern hardware makes performance analysis much harder than it used to be. In this short article we give three interesting examples to illustrate the limits of big O notation.
When it comes to performance, there are two ways to go: one is to improve the usage of the existing hardware resources, the other is to use the new hardware resources. We already talked a lot about how to increase the performance of your program by better using the existing resources, for example, by decreasing…
When processing (searching, inserting etc) your data structure, if you are accessing it in random-access fashion, the performance will suffer due to many data cache misses. Read on how to use the explicit data prefetching to speed up access to your data structure.
We investigate how the data cache influences the performance of your program, talk about ways for you to write faster programs by better leveraging the data cache.