More evidence that garbage collection is expensive

As seen on Lambda the Ultimate.org

Quantifying the Performance of Garbage Collection vs. Explicit Memory Management

We compare explicit memory management to both copying and non-copying garbage collectors across a range of benchmarks, and include real (non-simulated) runs that validate our results. These results quantify the time-space tradeoff of garbage collection: with five times as much memory, an Appel-style generational garbage collector with a non-copying mature space matches the performance of explicit memory management. With only three times as much memory, it runs on average 17% slower than explicit memory management. However, with only twice as much memory, garbage collection degrades performance by nearly 70%. When physical memory is scarce, paging causes garbage collection to run an order of magnitude slower than explicit memory management.


Comments

Post a Comment

Popular posts from this blog

GLES Quake - a port of Quake to the Android platform

I've just published the source code to a port of Quake to the Android platform . This is something I did a while ago as a internal test application. It's not very useful as a game, because there hasn't been any attempt to optimize the controls for the mobile phone. It's also awkward to install because the end user has to supply the Quake data files on their own. Still, I thought people might enjoy seeing a full-sized example of how to write a native code video game for the Android platform. So there it is, in all its retro glory. (Porting Quake II or Quake III is left as an exercise for the reader. :-) What's different about this particular port of Quake? Converted the original application into a DLL Android applications are written in Java, but they are allowed to call native languge DLLs, and the native language DLLs are allowed to call a limited selection of OS APIs, which include OpenGL ES and Linux File I/O. I was able to make the game work by using Java for: T
Read more

A Taipei-Torrent postmortem: Writing a BitTorrent client in Go

This is a BitTorrent client. There are many like it, but this one is mine. -- the BitTorrent Implementer's Creed For fun I've started writing a command-line BitTorrent client in Google's go programming language. The program, Taipei-Torrent , is about 70% done. It can successfully download a torrent, but there are still lots of edge cases to implement. Go routines and channels are a good base for writing multithreaded network code. My design uses goroutines as follows: a single main goroutine contains most of the BitTorrent client logic. each BitTorrent peer is serviced by two goroutines: one to read data from the peer, the other to write data to the peer. a goroutine is used to communicate with the tracker some "time.Tick" goroutines are used to wake the main goroutine up periodically to perform housekeeping duties. All the complicated data structures are owned by the main goroutine. The other goroutines just perform potentially blocking network I/O using channel
Read more

A Multi-threaded Go Raytracer

Image
Above is the output of the raytracer. Below is a diagnostic mode showing which goroutines raytraced which section of the screen. Each goroutine has its own color to outline the pixels it traces: I wrote a simple multi-threaded ray tracer in Google's new "go" language. It's an adaptation of Flying Frog Consultancy's Raytracer . It runs single-threaded about 1/2 the speed of a comparable C++ version. I guess the C++ version benefits from a more optimizing compiler and the ability to inline small functions. Compared to ordinary C/C++, the Go version was easier to multithread. On my dual-core Macbook Pro I get an 1.80x speedup when running with GOMAXPROCS > 1: $ GOMAXPROCS=1 time ./gotrace 1.52 real 1.50 user 0.01 sys $ GOMAXPROCS=2 time ./gotrace 0.82 real 1.50 user 0.01 sys $ GOMAXPROCS=3 time ./gotrace 0 .81 real 1.50 user 0.01 sys On an eight-core, 16 Hyperthread HP Z600 running Ubuntu 9
Read more