On 10/10/2019 05:41 PM, Murilo wrote:
> I have started working with neural networks and for that I need a lot of
> computing power but the programs I make only use around 30% of the cpu,
> or at least that is what Task Manager tells me. How can I make it use
> all 4 cores of my AMD FX-4300 and how can I make it use 100% of it?
Your threads must allocate as little memory as possible because memory
allocation can trigger garbage collection and garbage collection stops
all threads (except the one that's performing collection).
We studied the effects of different allocation schemes during our last
local D meetup[1]. The following program has two similar worker threads.
One allocates in an inner scope, the other one uses a static Appender
and clears its state as needed.
The program sets 'w' to 'worker' inside main(). Change it to 'worker2'
to see a huge difference: On my 4-core laptop its 100% versus 400% CPU
usage.
import std.random;
import std.range;
import std.algorithm;
import std.concurrency;
import std.parallelism;
enum inner_N = 100;
void worker() {
ulong result;
while (true) {
int[] arr;
foreach (j; 0 .. inner_N) {
arr ~= uniform(0, 2);
}
result += arr.sum;
}
}
void worker2() {
ulong result;
static Appender!(int[]) arr;
while (true) {
arr.clear();
foreach (j; 0 .. inner_N) {
arr ~= uniform(0, 2);
}
result += arr.data.sum;
}
}
void main() {
// Replace with 'worker2' to see the speedup
alias w = worker;
auto workers = totalCPUs.iota.map!(_ => spawn(&w)).array;
w();
}
The static Appender is thread-safe because each thread gets their own
copy due to data being thread-local by default in D. However, it doesn't
mean that the functions are reentrant: If they get called recursively
perhaps indirectly, then the subsequent executions would corrupt
previous executions' Appender states.
Ali
[1] https://www.meetup.com/D-Lang-Silicon-Valley/events/kmqcvqyzmbzb/
Are you someone in the Bay Area but do not come to our meetups? We've
been eating your falafel wraps! ;)
