I have prepared a few simple programs to compare Go with C in terms of speed and to play with concurrent programming in Go. First, let's have a look at a typical recursive Fibonacci example. Below is a C version:
#include <stdio.h> #include <stdlib.h> int fib(int n) { if (n < 2) { return(n); } return(fib(n-2) + fib(n-1)); } int main(int argc, char *argv[]) { int n = atoi(argv[1]); printf("%d\n", fib(n)); return(0); }and here is a Go version:
package main import ( "flag" "fmt" ) var f = flag.Int("f", 1, "Fibonacci number") func fib(n int) int { if n < 2 { return n } return fib(n-2) + fib(n-1) } func main() { flag.Parse() fmt.Println(fib(*f)) }A quick test shows that a single threaded Go program is actually faster than the C one:
$ gcc -O2 -o fib fib.c $ time ./fib 40 102334155 real 0m1.987s user 0m1.980s sys 0m0.004s $ 8g fib.go; 8l fib.8 $ time ./8.out -f=40 102334155 real 0m1.934s user 0m1.932s sys 0m0.004sI also prepared a program in Go to calculate a sum of subsequent Fibonacci sequences up to a given number in parallel. It uses run function as a goroutine to calculate each sequence independently and a shared channel ch to gather the results, that are finally summed up (so we don't care about the order in which they appear in the channel):
package main import ( "flag" "fmt" "runtime" ) var n = flag.Int("n", 1, "Number of CPUs to use") var f = flag.Int("f", 1, "Fibonacci number") func fib(n int) int { if n < 2 { return n } return fib(n-2) + fib(n-1) } func run(n int, ch chan int) { ch <- fib(n) } func main() { flag.Parse() runtime.GOMAXPROCS(*n) ch := make(chan int) for i := 0; i <= *f; i++ { go run(i, ch) } sum := 0 for i := 0; i <= *f; i++ { sum += <-ch } fmt.Println(sum) }The program takes additional parameter -n to indicate the number of CPU cores to use. According to Go runtime package documentation the call to GOMAXPROCS is temporary and will go away when the scheduler improves. Until then you have to remember to use this call, otherwise your application will use only one CPU core by default.
I ran the program for 40 Fibonacci sequences on dual Intel Xeon L5420 2.50GHz using from single up to all available CPU cores. The execution time improved most dramatically between -n=1 (5.073s) and -n=2 (3.141s), than it gradually slowed down from -n=3 (2.574s) to -n=8 (2.013s).
What I like about Go is that it gives a set of powerful tools into programmer's hands, but at the same time does not try to hide the complexity of parallel programming behind bloated libraries or awkward language constructs. It nicely follows the KISS principle and borrows some good ideas from Unix design (like channels, which work similar to Unix pipelines). If you think seriously about future system programming, I think Go is definitely a language worth learning. Not only because it's Google ;-)
No comments:
Post a Comment