You might be surprised at how much changing your for loops can be when accessing matrix data.

    for(int i=0; i<10000; i++){
        for(int j=0; j<10000; j++){
            blah[j][i] = whatever(); // Column oriented is very slow.
        }
    }

    for(int j=0; j<10000; j++){
        for(int i=0; i<10000; i++){ // Very fast: We move row-wise now. With luck, this is SIMD-vectorized by your compiler.
            blah[j][i] = whatever();
        }
    }

-----------

If someone is writing high performance code, the #1 goal is to be able to read your profiler's output. If you can't read what the profiler says (cache hits, TLB hits, memory stalls, etc. etc.) then you can't make sense of the data.

The PDF goes deeper than I'd personally go, but there's some concepts here that are absolutely necessary if you actually want to understand what a decent profiler gives you these days.

  % cat t.c
  #include <sys/time.h>
  #include <sys/resource.h>
  #include <stdio.h>

  double get_time()
  {
    struct timeval t;
    struct timezone tzp;
    gettimeofday(&t, &tzp);
    return t.tv_sec + t.tv_usec*1e-6;
  }

  int blah[SIZE][SIZE];
  int whatever() { static int i=0; return ++i; }

  int main()
  {
    double t0 = get_time();

    for(int i=0; i<SIZE; i++){
        for(int j=0; j<SIZE; j++){
            blah[j][i] = whatever(); // Column oriented is very slow.
        }
    }

    double t1 = get_time();

    for(int j=0; j<SIZE; j++){
        for(int i=0; i<SIZE; i++){ // Very fast: We move row-wise now. With luck, this is SIMD-vectorized by your compiler.
            blah[j][i] = whatever();
        }
    }

    double t2 = get_time();

    printf("SIZE=%5d dt1=%4.3e dt2=%4.3e\n",SIZE,t1-t0,t2-t1);

    return 0;
  }


  % for (( i=1; i<100000 ; i*=10 )) do gcc -DSIZE=$i -O t.c; ./a.out; done 
  SIZE=    1 dt1=4.179e-04 dt2=0.000e+00
  SIZE=   10 dt1=5.190e-04 dt2=0.000e+00
  SIZE=  100 dt1=5.062e-04 dt2=9.537e-07
  SIZE= 1000 dt1=4.014e-03 dt2=1.490e-04
  SIZE=10000 dt1=1.347e+00 dt2=4.349e-02

SIMD only really makes a difference at the L1 or L2 cache levels. Its a fancy micro-optimization that compilers do and can improve code speed in those cases...

But the "big" change, going from column-wise traversal into row-wise traversal, is the huge memory optimization that programmers should know about. It just so happens that SIMD-optimizations are also easier for compilers to figure out on row-wise traversal, so you get SIMD-optimization "for free" in many cases.

1. L1 cache lines are 64-bytes long. By fetching column-wise, you are wasting the bandwidth between L1 and main-memory. L1 cache will always fetch 64-bytes. By moving "with" the cache, you allow the L1 --> Main Memory data-transfers to be far more efficient.

2. Virtual Memory is translated by the TLB before it returns the actual value. Moving within a 4kB page is more efficient than moving across pages.

The non-reason:

* Hardware prefetcher probably works, even on column-oriented data.

All of these reasons hold even if the SIMD-optimizer fails. If the SIMD-optimizer is actually working, you'll more efficiently load/store to L1 cache. But this is likely a memory-bound problem and optimizing the core isn't as important.

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.

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."

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.

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.

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.

My list presupposes you've hit a wall and need the best performance you can get. That's not always the case, but I certainly wouldn't say that these optimizations don't make a difference in the "real world of softare."

False sharing alone can be the difference between a 16x parallelization speedup and a 1000x+ slowdown over the naive serial algorithm. 16x can be the difference between actionable results tomorrow or three weeks from today... and if you didn't know about it, the 1000x+ slowdown would be otherwise inscrutable.

I don't know exactly what that number is going to be, but I do know it is going to be a lot less than 100%.

"web applications/web sites/microservices"

The world is bigger than you think it is. There could be more code in making a single AAA videogame than in the entire Amazon infrastructure.

For example, an O(n^2) algorithm can often beat an O(n) one when n is "small". How do you get that "experienced" feel for which to choose when you're designing your program?

This is even important for people doing REST APIs, Web Services and Microservices. Milliseconds matter when the load gets beyond a certain point and your system ends up full of stragglers. How do you get that "experienced" feel for where that point might be and how far you can push a particular architecture without investing in a major rework?

The effect of tail latency on most web servers might surprise you:

http://latencytipoftheday.blogspot.com/

http://latencytipoftheday.blogspot.com/2014/06/latencytipoft...

TL;DR: most of your visitors will experience the bottom 1% of your performance curve on every page load.

Back when I started, both the BASIC and Common Lisp camps had heuristics on helping the compiler generate more efficient code. The reasons are similar to today: increase throughput for scaling or customer experience while simultaneously reducing what you spend on hardware and/or electricity. Lean, easy-to-manage setups can also sometimes let you afford extra personnel to build stuff faster.

There's definitely a cut-off where micro-optimizations wouldn't be necessary. A lot of efficiency gains are simple, though. Just gotta consistently use what you learn.