Okay, so today I’m gonna share my experience with something I’ve been tinkering with – 108 25. Sounds cryptic, right? Let me break it down.
It all started when I was trying to optimize some code I was working on. It was running way too slow, and I was pulling my hair out trying to figure out why. I’d tried the usual stuff – profiling, checking for obvious bottlenecks, the whole shebang. Nothing seemed to make a significant difference.
Then, I stumbled across a forum post talking about this technique called, let’s call it “reduction cycles.” The basic idea is to break down a complex calculation into smaller, more manageable chunks, then process those chunks in parallel. It sounded promising, so I decided to give it a shot.
First, I had to identify the core calculation that was slowing everything down. This took a while, because the code was a tangled mess. But eventually, I managed to isolate it. It was a nested loop that was iterating over a huge dataset, performing some kind of mathematical operation on each element.
Once I had the core calculation, I started thinking about how to break it down. The forum post suggested dividing the dataset into smaller chunks, then processing each chunk in a separate thread. So that’s what I did. I created a thread pool, and then I divided the dataset into 108 chunks. Why 108? No particular reason, really. It just seemed like a reasonable number.
Next, I assigned each chunk to a thread in the pool. Each thread would then perform the core calculation on its assigned chunk. Once all the threads were finished, I would combine the results to get the final answer.
The initial results were… disappointing. The code was still running slow. In fact, it was even slower than before! I was about to give up, but then I realized that I was spending a lot of time creating and destroying threads. This overhead was negating any gains I was getting from parallelism.
So, I decided to try something different. Instead of creating a new thread for each chunk, I reused the threads in the pool. This reduced the overhead significantly, and the code started to run much faster. But it still wasn’t as fast as I wanted it to be.
Then I remembered reading about something called “cache locality.” The idea is that if you access the same data multiple times in a row, it’s more likely to be stored in the CPU cache, which is much faster than main memory. So, I tried to rearrange the code to improve cache locality. Specifically, I made sure that each thread was accessing data that was close together in memory.
And that’s when things really started to improve. The code was now running significantly faster than it had been before. But I wasn’t done yet. I kept tweaking the code, trying different chunk sizes, different thread pool sizes, different memory access patterns. Eventually, I managed to get the code running 25 times faster than it had been originally. That’s where the “25” comes in.
So, that’s the story of 108 25. It was a long and frustrating process, but in the end, it was worth it. I learned a lot about parallelism, cache locality, and code optimization. And I also got a piece of code that runs a heck of a lot faster.
Here’s the gist of what I did:
- Identified a slow nested loop calculation
- Divided the dataset into 108 chunks.
- Used a thread pool to process each chunk in parallel.
- Reduced thread creation overhead by reusing threads.
- Improved cache locality by rearranging memory access.
- Achieved a 25x speedup.
I hope this helps someone out there struggling with slow code!