sutton036

Our previous post caused some controversy by questioning the validity of some commonly-held beliefs. Specifically, the beliefs that reference counting (RC) always requires less memory than tracing garbage collection (GC) and that tracing GCs require 3-4x more memory in order to perform adequately. We presented a benchmark written in Swift and OCaml and noted that the RC'd Swift implementation ran over 5x slower and required over 3x more memory than the tracing GC'd OCaml implementation. This observation disproves these beliefs in their strong form and even brings into question whether there is even any validity in the weak forms of those beliefs. After all, we have never seen any empirical evidence to support these beliefs in any form. We received a lot of criticism for that post. The vast majority of the criticism was not constructive but two valid points did arise. Firstly, although our result was anomalous it would be more compelling to see the benchmark repeated across a w...

The way software developers cling to folklore and avoid proper experimental testing of hypotheses really bothers me. I think perhaps the worst branch of computer science still riddled with myths and legends is memory management. Despite over half a century of research on garbage collection showing that reference counting is inferior to tracing garbage collections algorithms (even when almost all GC research restricts consideration to Java when much better algorithms have been known for years) there are still many people who claim otherwise. C++ developers obviously still believe that reference counted smart pointers are superior to tracing garbage collection but now other people are doing it too. People who should know better. This situation recently reared its ugly head again when Apple released a promising new programming language called Swift that shuns tracing garbage collection in favor of reference counting. Now, there are logical reasons for Apple to have chosen reference count...

Xavier Leroy’s standard lecture on threads post from the caml-list in 2002 seems to have disappeared from the INRIA archives so I am reproducing it here for anyone who is interested. This post is of historical interest because it explains why OCaml has no support for multicore parallelism to this day (twelve years after the release of the first consumer-level multicore CPUs). Date: 2002-11-25 (10:01) From: Xavier Leroy <xavier.leroy@i...> Subject: Re: [Caml-list] Why systhreads? It seems that the annual discussion on threads started again.  Allow me to deliver again my standard lecture on this topic. Threads have at least three different purposes: 1- Parallelism on shared-memory multiprocessors. 2- Overlapping I/O and computation (while a thread is blocked on a network    read, other threads may proceed). 3- Supporting the "coroutine" programming style    (e.g. if a program has a GUI but performs long computations,     using threads is a nicer wa...

I have been asked to elaborate on my answer on Quora so here goes:   1.        There is no efficient purely functional unsorted dictionary or set Since the 1990s the use of dictionaries in software has gone through the roof. Dictionaries are now a stock collection type that every programmer expects to find in their standard library. Purely functional or persistent data structures such as those found in Okasaki’s fabulous monograph on the subject can be a great tool. In particular, the persistence they offer means you can reuse old versions of collections without having to worry about mutation. In many cases (particularly for some kinds of problems such as logic programming and compiler writing) this can make solutions shorter and clearer, partly because it makes backtracking trivial. However, persistence comes at a great cost in terms of performance: purely functional dictionaries are typically 10x slower than a decent hash table and I have seen...

I previously looked at x86 code generation quality and found that GCC does some interesting high-level optimisations but, in that case, any performance benefit was lost in poor quality code generation. In this article I’m going to look at ARM assembly instead.   Compiling the Fibonacci function in C from last time we obtain times ranging from 2.4s to 6.6s for fib(40) using –O2 to –O0. Interestingly, using –O3 actually worsens performance over –O2. As before, inspection of the generated assembly language shows that GCC employs nifty high-level optimisations when given the –O2 option.   The simplest implementation of our Fibonacci function in ARM assembly is perhaps:   fib:         cmp     r0, #2         movlt   pc, lr         stmfd   sp!, {r1, r2, lr}         s...

Another deleted answer of mine from Stack Overflow: As I can see, smart pointers are used extensively in many real-world C++ projects. True but, objectively, the vast majority of code is now written in modern languages with tracing garbage collectors. Though some kind of smart pointers are obviously beneficial to support RAII and ownership transfers, there is also a trend of using shared pointers by default, as a way of "garbage collection", so that the programmer do not have to think about allocation that much. That's a bad idea because you still need to worry about cycles. Why are shared pointers more popular than integrating a proper garbage collector like Boehm GC? (Or do you agree at all, that they are more popular than actual GCs?) Oh wow, there are so many things wrong with your line of thinking: Boehm's GC is not a "proper" GC in any sense of the word. It is truly awful. It is conservative so it leaks and is inefficient by design. See: http://flying...

The following answer to a question about C++ vs C# performance on Stack Overflow has sadly been deleted despite having 305 upvotes:   I often heard that people prefer C++ to C# mainly in the performance critical code,because the GC might turn up on critical path, causing the performance penalty. I have heard that in some circles but never respectable circles. For example, I consulted for a company in London who were selling stock exchange software that had been written in 1,000,000 lines of C++. Over 40 developers had been working on it for almost 15 years and they were convinced that C++ was the correct solution for such software because latency and throughput performance were both critical. They were achieving latencies as low as 50ms (with a single trader connected!) and throughput as high as 10k trades per second (tps). However, they were struggling to support more than 2,000 traders because they had several threads per trader (no async) and, in fact, traders were reporting lat...