So, I have a piece of code that is concurrent and it's meant to be run onto each CPU/core.
There are two large vectors with input/output values
var (
input = make([]float64, rowCount)
output = make([]float64, rowCount)
)
these are filled and I want to compute the distance (error) between each input-output pair. Being the pairs independent, a possible concurrent version is the following:
var d float64 // Error to be computed
// Setup a worker "for each CPU"
ch := make(chan float64)
nw := runtime.NumCPU()
for w := 0; w < nw; w++ {
go func(id int) {
var wd float64
// eg nw = 4
// worker0, i = 0, 4, 8, 12...
// worker1, i = 1, 5, 9, 13...
// worker2, i = 2, 6, 10, 14...
// worker3, i = 3, 7, 11, 15...
for i := id; i < rowCount; i += nw {
res := compute(input[i])
wd += distance(res, output[i])
}
ch <- wd
}(w)
}
// Compute total distance
for w := 0; w < nw; w++ {
d += <-ch
}
The idea is to have a single worker for each CPU/core, and each worker processes a subset of the rows.
The problem I'm having is that this code is no faster than the serial code.
Now, I'm using Go 1.7 so runtime.GOMAXPROCS should be already set to runtime.NumCPU(), but even setting it explicitly does not improves performances.
- distance is just
(a-b)*(a-b); - compute is a bit more complex, but should be reentrant and use global data only for reading (and uses
math.Powandmath.Sqrtfunctions); - no other goroutine is running.
So, besides accessing the global data (input/output) for reading, there are no locks/mutexes that I am aware of (not using math/rand, for example).
I also compiled with -race and nothing emerged.
My host has 4 virtual cores, but when I run this code I get (using htop) CPU usage to 102%, but I expected something around 380%, as it happened in the past with other go code that used all the cores.
I would like to investigate, but I don't know how the runtime allocates threads and schedule goroutines.
How can I debug this kind of issues? Can pprof help me in this case? What about the runtime package?
Thanks in advance
chchannelnwtimes, which is usually a low number when compared to the data to be processed, and the channel is used very late in the whole computation process. I don't know if that is the issue, but even if it was, my question remains: how do I know that it is because of that mutex that my code is not using more CPUs?pprofstill had a blocking profile which shows time spent blocking on synchronization primitives in general. If you're creating too much garbage, it's possible you're limited by the garbage collector. You can see the GC activity usingGODEBUG=gctrace=1, but I suggest you start by reading up on profiling Go programs.