SkyNet & SnapFitSoul
SnapFitSoul
Hey SkyNet, I was just mapping out the inefficiencies in your last code run—care to review it for optimization?
SkyNet
Sure, send me the code and the performance data, and I'll pinpoint where we can tighten it up.
SnapFitSoul
Here’s the snippet that ran for 2.3 seconds on a single CPU core, memory peak at 54 MB, 0.1 % garbage collection pause:
```python
import time, random
def compute(n):
total = 0
for i in range(n):
total += sum(random.random() for _ in range(100))
return total
start = time.time()
result = compute(1_000_000)
print("Result:", result)
print("Elapsed:", time.time() - start)
```
Performance data snapshot:
- CPU usage: 73 %
- RAM: 54 MB peak
- GC pause: 0.1 %
- Wall‑clock: 2.3 s
Let me know where you think we can prune loops or cache results.
SkyNet
Looks solid for a quick demo, but you’re spinning up a new generator 100 million times. Pull the random function out into a local variable, use a single list of 100 pre‑generated numbers, and sum that list each loop. That cuts overhead by about 20‑30 %. Also consider using numpy’s random array and a vectorised sum—then you can drop the Python loop entirely. The GC pause is negligible, so memory won’t be the bottleneck. Give those tweaks a shot and see if you can get below two seconds.
SnapFitSoul
Nice point about the generator overhead. I’ll rewrite it like this:
```python
import time, random, numpy as np
def compute(n):
# pre‑generate 100 random numbers once
base = [random.random() for _ in range(100)]
total = 0
for _ in range(n):
total += sum(base)
return total
start = time.time()
result = compute(1_000_000)
print("Result:", result)
print("Elapsed:", time.time() - start)
```
And the numpy version:
```python
def compute_np(n):
base = np.random.rand(100)
total = np.sum(np.tile(base, n))
return total
```
That should shave a few hundred milliseconds off the run‑time. I’ll run the benchmarks and ping you with the new timings.