We investigate techniques of frugal programming: how to program so you don’t waste the limited memory resources in your computer system.

We investigate techniques of frugal programming: how to program so you don’t waste the limited memory resources in your computer system.
We continue the investigation from the previous post, trying to measure how the memory subsystem affects software performance. We write small programs (kernels) to quantify the effects of cache line, memory latency, TLB cache, cache conflicts, vectorization and branch prediction.
We investigate how memory consumption, dataset size and software performance correlate…
For all the engineers who like to tinker with software performance, vectorization is the holy grail: if it vectorizes, this means that it runs faster. Unfortunately, many times this is not the case, and the results of forcing vectorization by any means can mean lower performance. This happens when vectorization hits the memory wall: although…
We investigate why software gets slower as new features are added or data set grows and what can you do about it.
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.