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If I saw that code at my company, I'd ask why someone building a rest api for web-services had to iterate through a 10000*10000 2D array while calling a method

If I saw that code at my company, I'd ask why someone building a rest api for web-services had to iterate through a 10000*10000 2D array while calling a method of every single cell. This isn't a use case I've seen in my 10 year career thus far. Also, you have to understand that saving 1 or 2 milliseconds isn't important when compared to getting software built faster. For the typical use case, optimizing a for-loop like this for in-memory data isn't going to change the total performance of your program by even 0.5%. This is what so many people just don't understand. For modern web based applications, 99.9% of the time spent is doing IO, either hard-drive, data storage, or across the network. Optimizing those calls is FAR more important, than wasting time trying to save 1 millisecond in a for-loop which is iterating over 100 in-memory objects. This is where the understanding that the practical reality of building software for a real business is very different to theoretical examples drummed up by computer scientists on discussion boards. I see so many engineers trying to make their in-memory code faster by parallizing it, saving (if they're lucky) 1 or 2 milliseconds, but making their code far less maintainable and potentially introducing bugs, while the users wouldn't notice any practical difference in the app. Meanwhile, the app really does feel slow, because some numb-nuts made a call to some api and blocked the ui thread.

magoghm 8y ago
Were I work we have to process a few billion records per day. Knowing about how memory works and writing the code in a way that uses memory bandwidth efficiently, allows us to process all those records in smallish AWS instance instead of having to use Hadoop on a much more expensive cluster of instances.
- saberience 8y ago
  Hadoop is for "big data." Big data is commonly defined as minimum of petabytes of data. That is, thousands of terabytes. Our ingest of new events to our cluster is upwards of 25B per day and we keep all that data in perpetuity. Our hadoop cluster is relatively small at 5PB right now, but I would be impressed indeed if you were processing PB of data on a smallish AWS instance. If you were considering using Hadoop for work that can be processed on a "smallish instance" then I'd suggest you maybe don't really understand big-data or the normal workloads for which Hadoop is intended. For example, we run ML models on our Hadoop cluster which take hours to run distributed over around 100 nodes. Let me know when this can be done on a "smallish instance."
  - dragontamer 8y ago
    I don't work in big data. But here's what I think of your field anyway :-) In general, you optimize software by starting from the slowest things first, and then working your way to the faster things when you run out of things to optimize. It seems like your problems are I/O bound, and therefore the bulk of optimizations can be made by simply optimizing your I/O (either through async calls, or better understanding of what the frameworks are actually doing, etc. etc.). And that makes sense for sure. The thing is: the next level of optimization is not CPU-optimization, but instead memory optimization. In fact, all CPU-based optimization starts at the Main-memory level. Why? Because memory is slower than virtually everything inside of the main CPU Core. Which means, memory-level optimization is a far more important skill than any other CPU-based optimization technique. Modern CPU Cores operate at 4GHz easily, but RAM only responds every 100ns on server systems (that's 400 CPU-cycles of RAM Latency!) ----------- In effect, if there's one low-level optimization any higher-level programmer should know about, it is RAM optimization. Sure, there are CPU-optimizations (SIMD registers, L1 vs L2 cache, and more), but RAM access itself is hundreds of times slower than CPU-speeds and needs to be optimized before lower-level optimizations are sought. Once RAM access are fully optimized, then you can finally reach for the CPU-core optimizations. Instruction-level parallelism, or SIMD Registers, or what-not. RAM is the next slowest part of your system after I/O. As such, optimizing RAM access is the most logical next step forward when your programs are running poorly.
drieddust 8y ago
> If I saw that code at my company, I'd ask why someone building a rest api for web-services had to iterate through a 10000*10000 2D array while calling a method of every single cell Why not? I work with the scripting languages only and regularly hit this scenario so I won't say this is completely useless. When you analyze a few hundred 50GB files for specific patterns, you have to go line by line in those cases shaving off milliseconds, optimizing how data should be accessed and optimized becomes valuable.
gdy 8y ago
I've seen the code written by a Python web-programmer that for a given binary matrix of teams and tournaments produced top 10 rivals for each team where rivals are defined as teams that have picked the same tournaments to participate as your team. There are tens of thousands teams and thousands of tournaments and his code was taking two days to precalculate the results for every team. Written with minimal understanding of how slow the memory is, the new code takes less than a minute. There was no point of optimizing it further, the algorithmic changes were enough.

magoghm 8y ago

Were I work we have to process a few billion records per day. Knowing about how memory works and writing the code in a way that uses memory bandwidth efficiently, allows us to process all those records in smallish AWS instance instead of having to use Hadoop on a much more expensive cluster of instances.

saberience 8y ago
Hadoop is for "big data." Big data is commonly defined as minimum of petabytes of data. That is, thousands of terabytes. Our ingest of new events to our cluster is upwards of 25B per day and we keep all that data in perpetuity. Our hadoop cluster is relatively small at 5PB right now, but I would be impressed indeed if you were processing PB of data on a smallish AWS instance. If you were considering using Hadoop for work that can be processed on a "smallish instance" then I'd suggest you maybe don't really understand big-data or the normal workloads for which Hadoop is intended. For example, we run ML models on our Hadoop cluster which take hours to run distributed over around 100 nodes. Let me know when this can be done on a "smallish instance."
- dragontamer 8y ago
  I don't work in big data. But here's what I think of your field anyway :-) In general, you optimize software by starting from the slowest things first, and then working your way to the faster things when you run out of things to optimize. It seems like your problems are I/O bound, and therefore the bulk of optimizations can be made by simply optimizing your I/O (either through async calls, or better understanding of what the frameworks are actually doing, etc. etc.). And that makes sense for sure. The thing is: the next level of optimization is not CPU-optimization, but instead memory optimization. In fact, all CPU-based optimization starts at the Main-memory level. Why? Because memory is slower than virtually everything inside of the main CPU Core. Which means, memory-level optimization is a far more important skill than any other CPU-based optimization technique. Modern CPU Cores operate at 4GHz easily, but RAM only responds every 100ns on server systems (that's 400 CPU-cycles of RAM Latency!) ----------- In effect, if there's one low-level optimization any higher-level programmer should know about, it is RAM optimization. Sure, there are CPU-optimizations (SIMD registers, L1 vs L2 cache, and more), but RAM access itself is hundreds of times slower than CPU-speeds and needs to be optimized before lower-level optimizations are sought. Once RAM access are fully optimized, then you can finally reach for the CPU-core optimizations. Instruction-level parallelism, or SIMD Registers, or what-not. RAM is the next slowest part of your system after I/O. As such, optimizing RAM access is the most logical next step forward when your programs are running poorly.

drieddust 8y ago

> If I saw that code at my company, I'd ask why someone building a rest api for web-services had to iterate through a 10000*10000 2D array while calling a method of every single cell Why not? I work with the scripting languages only and regularly hit this scenario so I won't say this is completely useless. When you analyze a few hundred 50GB files for specific patterns, you have to go line by line in those cases shaving off milliseconds, optimizing how data should be accessed and optimized becomes valuable.

gdy 8y ago

I've seen the code written by a Python web-programmer that for a given binary matrix of teams and tournaments produced top 10 rivals for each team where rivals are defined as teams that have picked the same tournaments to participate as your team. There are tens of thousands teams and thousands of tournaments and his code was taking two days to precalculate the results for every team. Written with minimal understanding of how slow the memory is, the new code takes less than a minute. There was no point of optimizing it further, the algorithmic changes were enough.