I'm new to the Nim language. I wanted to learn it by implementing a simple Genetic Algorithm to evolve strings (array of integers atm) by distributing the work to the CPU cores:
https://github.com/peheje/nim_genetic
I have successfully parallelized the creation of the "Agents", but cannot do it to call a function "life" that must mutate the passed in state:
...
type
Agent* = ref object
data*: seq[int]
fitness*: float
...
var pool: seq[Agent] = newAgentsParallel(POPULATION_SIZE)
# Run generations
for gen in 0..<N_GENERATIONS:
let wheel = createWheel(pool)
let partitions: seq[seq[Agent]] = partition(pool, N_THREADS)
parallel:
for part in partitions:
echo "spawning"
spawn life(part, pool, wheel)
pool = createPool(pool, wheel)
...
proc life(part: seq[Agent], pool: seq[Agent], wheel: seq[float]) =
for a in part:
if random(1.0) < CROSSOVER_PROP:
a.crossover(pool, wheel)
if random(1.0) < MUTATE_PROP:
a.mutate()
a.calcFitness()
echo "life done"
CPU is bound at 100 % and it seems like Nim is copying the data to "life" as the RAM usage skyrockets after "spawning". In the Nim manual it says about the parallel block:
"Every other complex location loc that is used in a spawned proc (spawn f(loc)) has to be immutable for the duration of the parallel section. This is called the immutability check. Currently it is not specified what exactly "complex location" means. We need to make this an optimization!"
And I'm very much using it as mutable state, so maybe that is why Nim is copying the data? How can I get around passing pointers only? A few points:
- I guess I could avoid mutating, instead returning new instances that are modified, but I still need to pass in the pool and wheel to read from.
- If the parallel: statement is not possible to use, how would I implement it using threads?
- Is random() threadsafe? How else?
- Anything else I could do different? E.g. easier unwrapping of the FlowVar?
Coming from Kotlin with Java8 Streams I feel really spoiled.
