http://lukewassink.com/tags/scala/Recent content in Scala on Luke WassinkHugoen-usSun, 06 Sep 2026 15:33:45 -0400http://lukewassink.com/posts/categories-to-types/Sun, 06 Sep 2026 15:33:45 -0400http://lukewassink.com/posts/categories-to-types/<h2 id="who-is-this-for"> Who is this for? <a class="heading-link" href="#who-is-this-for"> <i class="fa-solid fa-link" aria-hidden="true" title="Link to heading"></i> <span class="sr-only">Link to heading</span> </a> </h2> <p>At this point, there are, by conservative estimate, 80,000 blog posts and Youtube videos that explain how monads fit into functional programming. It seems to be mandated by federal law that 99% of them begin by reassuring you that, unlike all the other explanations out there, <em>this explanation</em> won&rsquo;t use any fancy math or require any knowledge of category theory. This blog post belongs to the remaining 1%.</p>http://lukewassink.com/posts/matrix-multiplication-optimization/Thu, 03 Sep 2026 16:58:27 -0500http://lukewassink.com/posts/matrix-multiplication-optimization/<h1 id="optimizing-matrix-multiplication"> Optimizing Matrix Multiplication <a class="heading-link" href="#optimizing-matrix-multiplication"> <i class="fa-solid fa-link" aria-hidden="true" title="Link to heading"></i> <span class="sr-only">Link to heading</span> </a> </h1> <p>Matrix multiplication is a great test case to understand performance optimization. The underlying algorithm is fairly simple, and it benefits significantly from some classic optimizations like:</p> <ul> <li>reducing array access</li> <li>sequential memory access</li> <li>cache locality</li> <li>parallelization</li> </ul> <p>There are also fancy algorithms that reduce the asymptotic complexity, but they are so complex they usually only show benefits for very large matrices, if at all. In any case, our plan will be to use the traditional algorithm for matrix multiplication and explore the benefits we can get from other changes like: how we store the matrices, what order we do the computations in, and running parts of the computation in parallel.</p>