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Here is a piece of C++ code that shows some very peculiar performance. For some strange reason, sorting the data miraculously speeds up the code by almost 6x:

#include <algorithm>
#include <ctime>
#include <iostream>

int main()
    // Generate data
    const unsigned arraySize = 32768;
    int data[arraySize];

    for (unsigned c = 0; c < arraySize; ++c)
        data[c] = std::rand() % 256;

    // !!! With this, the next loop runs faster
    std::sort(data, data + arraySize);

    // Test
    clock_t start = clock();
    long long sum = 0;

    for (unsigned i = 0; i < 100000; ++i)
        // Primary loop
        for (unsigned c = 0; c < arraySize; ++c)
            if (data[c] >= 128)
                sum += data[c];

    double elapsedTime = static_cast<double>(clock() - start) / CLOCKS_PER_SEC;

    std::cout << elapsedTime << std::endl;
    std::cout << "sum = " << sum << std::endl;
  • Without std::sort(data, data + arraySize);, the code runs in 11.54 seconds.
  • With the sorted data, the code runs in 1.93 seconds.

Initially I thought this might be just a language or compiler anomaly. So I tried it in Java:

import java.util.Arrays;
import java.util.Random;

public class Main
    public static void main(String[] args)
        // Generate data
        int arraySize = 32768;
        int data[] = new int[arraySize];

        Random rnd = new Random(0);
        for (int c = 0; c < arraySize; ++c)
            data[c] = rnd.nextInt() % 256;

        // !!! With this, the next loop runs faster

        // Test
        long start = System.nanoTime();
        long sum = 0;

        for (int i = 0; i < 100000; ++i)
            // Primary loop
            for (int c = 0; c < arraySize; ++c)
                if (data[c] >= 128)
                    sum += data[c];

        System.out.println((System.nanoTime() - start) / 1000000000.0);
        System.out.println("sum = " + sum);

with a similar but less extreme result.

My first thought was that sorting brings the data into cache, but my next thought was how silly that is because the array was just generated.

What is going on? Why is a sorted array faster than an unsorted array? The code is summing up some independent terms, the order should not matter.

Answered By: Mysticial ( 6432)

You are the victim of branch prediction fail.

What is Branch Prediction?

Consider a railroad junction:

Image by Mecanismo, from Wikimedia Commons:

Now for the sake of argument, suppose this is back in the 1800s - before long distance or radio communication.

You are the operator of a junction and you hear a train coming. You have no idea which way it will go. You stop the train to ask the captain which direction he wants. And then you set the switch appropriately.

Trains are heavy and have a lot of momentum. So they take forever to start up and slow down.

Is there a better way? You guess which direction the train will go!

  • If you guessed right, it continues on.
  • If you guessed wrong, the captain will stop, back up, and yell at you to flip the switch. Then it can restart down the other path.

If you guess right every time, the train will never have to stop.
If you guess wrong too often, the train will spend a lot of time stopping, backing up, and restarting.

Consider an if-statement: At the processor level, it is a branch instruction:

enter image description here

You are a processor and you see a branch. You have no idea which way it will go. What do you do? You halt execution and wait until the previous instructions are complete. Then you continue down the correct path.

Modern processors are complicated and have long pipelines. So they take forever to "warm up" and "slow down".

Is there a better way? You guess which direction the branch will go!

  • If you guessed right, you continue executing.
  • If you guessed wrong, you need to flush the pipeline and roll back to the branch. Then you can restart down the other path.

If you guess right every time, the execution will never have to stop.
If you guess wrong too often, you spend a lot of time stalling, rolling back, and restarting.

This is branch prediction. I admit it's not the best analogy since the train could just signal the direction with a flag. But in computers, the processor doesn't know which direction a branch will go until the last moment.

So how would you strategically guess to minimize the number of times that the train must back up and go down the other path? You look at the past history! If the train goes left 99% of the time, then you guess left. If it alternates, then you alternate your guesses. If it goes one way every 3 times, you guess the same...

In other words, you try to identify a pattern and follow it. This is more or less how branch predictors work.

Most applications have well-behaved branches. So modern branch predictors will typically achieve >90% hit rates. But when faced with unpredictable branches with no recognizable patterns, branch predictors are virtually useless.

Further Reading:

As hinted from above, the culprit is this if-statement:

if (data[c] >= 128)
    sum += data[c];

Notice that the data is evenly distributed between 0 and 255. When the data is sorted, roughly the first half of the iterations will not enter the if-statement. After that, they will all enter the if-statement.

This is very friendly to the branch predictor since the branch consecutively goes the same direction many times. Even a simple saturating counter will correctly predict the branch except for the few iterations after it switches direction.

Quick visualization:

T = branch taken
N = branch not taken

data[] = 0, 1, 2, 3, 4, ... 126, 127, 128, 129, 130, ... 250, 251, 252, ...
branch = N  N  N  N  N  ...   N    N    T    T    T  ...   T    T    T  ...

       = NNNNNNNNNNNN ... NNNNNNNTTTTTTTTT ... TTTTTTTTTT  (easy to predict)

However, when the data is completely random, the branch predictor is rendered useless because it can't predict random data. Thus there will probably be around 50% misprediction. (no better than random guessing)

data[] = 226, 185, 125, 158, 198, 144, 217, 79, 202, 118,  14, 150, 177, 182, 133, ...
branch =   T,   T,   N,   T,   T,   T,   T,  N,   T,   N,   N,   T,   T,   T,   N  ...

       = TTNTTTTNTNNTTTN ...   (completely random - hard to predict)

So what can be done?

If the compiler isn't able to optimize the branch into a conditional move, you can try some hacks if you are willing to sacrifice readability for performance.


if (data[c] >= 128)
    sum += data[c];


int t = (data[c] - 128) >> 31;
sum += ~t & data[c];

This eliminates the branch and replaces it with some bitwise operations.

(Note that this hack is not strictly equivalent to the original if-statement. But in this case, it's valid for all the input values of data[].)

Benchmarks: Core i7 920 @ 3.5 GHz

C++ - Visual Studio 2010 - x64 Release

//  Branch - Random
seconds = 11.777

//  Branch - Sorted
seconds = 2.352

//  Branchless - Random
seconds = 2.564

//  Branchless - Sorted
seconds = 2.587

Java - Netbeans 7.1.1 JDK 7 - x64

//  Branch - Random
seconds = 10.93293813

//  Branch - Sorted
seconds = 5.643797077

//  Branchless - Random
seconds = 3.113581453

//  Branchless - Sorted
seconds = 3.186068823


  • With the Branch: There is a huge difference between the sorted and unsorted data.
  • With the Hack: There is no difference between sorted and unsorted data.
  • In the C++ case, the hack is actually a tad slower than with the branch when the data is sorted.

A general rule of thumb is to avoid data-dependent branching in critical loops. (such as in this example)

Update :

  • GCC 4.6.1 with -O3 or -ftree-vectorize on x64 is able to generate a conditional move. So there is no difference between the sorted and unsorted data - both are fast.

  • VC++ 2010 is unable to generate conditional moves for this branch even under /Ox.

  • Intel Compiler 11 does something miraculous. It interchanges the two loops, thereby hoisting the unpredictable branch to the outer loop. So not only is it immune the mispredictions, it is also twice as fast as whatever VC++ and GCC can generate! In other words, ICC took advantage of the test-loop to defeat the benchmark...

  • If you give the Intel Compiler the branchless code, it just out-right vectorizes it... and is just as fast as with the branch (with the loop interchange).

This goes to show that even mature modern compilers can vary wildly in their ability to optimize code...


This question attempts to collect the few pearls among the dozens of bad C++ books that are released every year.

Unlike many other programming languages, which are often picked up on the go from tutorials found on the Internet, few are able to quickly pick up C++ without studying a good C++ book. It is way too big and complex for doing this. In fact, it is so big and complex, that there are very many very bad C++ books out there. And we are not talking about bad style, but things like sporting glaringly obvious factual errors and promoting abysmally bad programming styles. And it's even worse with online tutorials. (There is a reason nobody bothered to setup a similar question for online tutorials.)

Please provide quality books and an approximate skill level — preferably after discussing your addition in the C++ chat room. (The regulars might mercilessly undo your work if they disagree with a recommendation.) Add a short blurb/description about each book that you have personally read/benefited from. Feel free to debate quality, headings, etc. Books that meet the criteria will be added to the list. Books that have reviews by the Association of C and C++ Users (ACCU) have links to the review.

To spell it out bluntly: There is no need to add a 75th answer to this question. If you feel like a book should be added, suggest it to the community and let's discuss it.

Note: FAQs and other resources can be found in the C++ tag info and under . There is also a similar post for C: The Definitive C Book Guide and List

Reference Style - All Levels

  1. The C++ Programming Language (Bjarne Stroustrup) (soon to be updated for C++11) The classic introduction to C++ by its creator. Written to parallel the classic K&R, this indeed reads very much alike it and covers just about everything from the core language to the standard library, to programming paradigms to the language's philosophy. (Thereby making the latest editions break the 1k page barrier.) [Review] The fourth edition (to be released in 2013) will cover C++11.

  2. C++ Standard Library Tutorial and Reference (Nicolai Josuttis) (updated for C++11) The introduction and reference for the C++ Standard Library. The second edition (released on April 9, 2012) covers C++11. [Review]

  3. The C++ IO Streams and Locales (Angelika Langer and Klaus Kreft) There's very little to say about this book except that, if you want to know anything about streams and locales, then this is the one place to find definitive answers. [Review]

C++ 11 References:

  1. The C++ Standard (INCITS/ISO/IEC 14882-2011) This, of course, is the final arbiter of all that is or isn't C++. Be aware, however, that it is intended purely as a reference for experienced users willing to devote considerable time and effort to its understanding. As usual, the first release was quite expensive ($300+ US), but it has now been released in electronic form for $30US -- probably the least expensive of the reference books listed here.

  2. Overview of the New C++ (C++11) By Scott Meyers, who's a highly respected author on C++. Even though the list of items is short, the quality is high.



If you are new to programming or if you have experience in other languages and are new to C++, these books are highly recommended.

  1. C++ Primer† (Stanley Lippman, Josée Lajoie, and Barbara E. Moo) (updated for C++11) Coming at 1k pages, this is a very thorough introduction into C++ that covers just about everything in the language in a very accessible format and in great detail. The fifth edition (released August 16, 2012) covers C++11. [Review]

  2. Accelerated C++ (Andrew Koenig and Barbara Moo) This basically covers the same ground as the C++ Primer, but does so on a fourth of its space. This is largely because it does not attempt to be an introduction to programming, but an introduction to C++ for people who've previously programmed in some other language. It has a steeper learning curve, but, for those who can cope with this, it is a very compact introduction into the language. (Historically, it broke new ground by being the first beginner's book using a modern approach at teaching the language.) [Review]

  3. Thinking in C++ (Bruce Eckel) Two volumes; second is more about standard library, but still very good

  4. Programming: Principles and Practice Using C++ (Bjarne Stroustrup) An introduction to programming using C++ by the creator of the language. A good read, that assumes no previous programming experience, but is not only for beginners.

† Not to be confused with C++ Primer Plus (Stephen Prata), with a significantly less favorable review.

Best practices

  1. Effective C++ (Scott Meyers) This was written with the aim of being the best second book C++ programmers should read, and it succeeded. Earlier editions were aimed at programmers coming from C, the third edition changes this and targets programmers coming from languages like Java. It presents ~50 easy-to-remember rules of thumb along with their rationale in a very accessible (and enjoyable) style. [Review]

  2. Effective STL (Scott Meyers) This aims to do the same to the part of the standard library coming from the STL what Effective C++ did to the language as a whole: It presents rules of thumb along with their rationale. [Review]


  1. More Effective C++ (Scott Meyers) Even more rules of thumb than Effective C++. Not as important as the ones in the first book, but still good to know.

  2. Exceptional C++ (Herb Sutter) Presented as a set of puzzles, this has one of the best and thorough discussions of the proper resource management and exception safety in C++ through Resource Acquisition is Initialization (RAII) in addition to in-depth coverage of a variety of other topics including the pimpl idiom, name lookup, good class design, and the C++ memory model. [Review]

  3. More Exceptional C++ (Herb Sutter) Covers additional exception safety topics not covered in Exceptional C++, in addition to discussion of effective object oriented programming in C++ and correct use of the STL. [Review]

  4. Exceptional C++ Style (Herb Sutter) Discusses generic programming, optimization, and resource management; this book also has an excellent exposition of how to write modular code in C++ by using nonmember functions and the single responsibility principle. [Review]

  5. C++ Coding Standards (Herb Sutter and Andrei Alexandrescu) "Coding standards" here doesn't mean "how many spaces should I indent my code?" This book contains 101 best practices, idioms, and common pitfalls that can help you to write correct, understandable, and efficient C++ code. [Review]

  6. C++ Templates: The Complete Guide (David Vandevoorde and Nicolai M. Josuttis) This is the book about C++ templates. It covers everything from the very basics to some of the most advanced template metaprogramming and explains every detail of how templates work (both conceptually and at how they are implemented) and discusses many common pitfalls. Has excellent summaries of the One Definition Rule (ODR) and overload resolution in the appendices. [Review]

Above Intermediate

  1. Modern C++ Design (Andrei Alexandrescu) A groundbreaking book on advanced generic programming techniques. Introduces policy-based design, type lists, and fundamental generic programming idioms then explains how many useful design patterns (including small object allocators, functors, factories, visitors, and multimethods) can be implemented efficiently, modularly, and cleanly using generic programming. [Review]

  2. C++ Template Metaprogramming (David Abrahams and Aleksey Gurtovoy)

  3. C++ Concurrency In Action (Anthony Williams) A book covering C++11 concurrency support including the thread library, the atomics library, the C++ memory model, locks and mutexes, as well as issues of designing and debugging multithreaded applications.

  4. Advanced C++ Metaprogramming (Davide Di Gennaro) A pre-C++11 manual of TMP techniques, focused more on practice than theory. There are a ton of snippets in this book, some of which are made obsolete by typetraits, but the techniques, are nonetheless, useful to know. If you can put up with the quirky formatting/editing, it is easier to read than Alexandrescu, and arguably, more rewarding. For more experienced developers, there is a good chance that you may pick up something about a dark corner of C++ (a quirk) that usually only comes about through extensive experience.

Classics / Older

Note: Some information contained within these books may not be up to date or no longer considered best practice.

  1. The Design and Evolution of C++ (Bjarne Stroustrup) If you want to know why the language is the way it is, this book is where you find answers. This covers everything before the standardization of C++.

  2. Ruminations on C++ - (Andrew Koenig and Barbara Moo) [Review]

  3. Advanced C++ Programming Styles and Idioms (James Coplien) A predecessor of the pattern movement, it describes many C++-specific "idioms". It's certainly a very good book and still worth a read if you can spare the time, but quite old and not up-to-date with current C++.

  4. Large Scale C++ Software Design (John Lakos) Lakos explains techniques to manage very big C++ software projects. Certainly a good read, if it only was up to date. It was written long before C++98, and misses on many features (e.g. namespaces) important for large scale projects. If you need to work in a big C++ software project, you might want to read it, although you need to take more than a grain of salt with it. There's been the rumor that Lakos is writing an up-to-date edition of the book for years.

  5. Inside the C++ Object Model (Stanley Lippman) If you want to know how virtual member functions are commonly implemented and how base objects are commonly laid out in memory in a multi-inheritance scenario, and how all this affects performance, this is where you will find thorough discussions of such topics.

This question has historical significance, but is not a good example of an appropriate question. Read and learn from this post, but please do not use it as evidence that you can ask similar questions.

See the FAQ for more info.

Answered By: Johannes Schaub - litb ( 379)

About C++ Templates The Complete Guide

I can only say good things about it. Written by two top experts. And you really notice that. The examples are well written, explained and placed.

They start by teaching you the basics on a few pages. Then they talk about all the pitfalls and hidden danger there is in template programming and the cool stuff that can be done with them. At the end, they explain in detail the overload resolution and the one definition rule in Appendix A and B.

Strongly recommended :)


After reading "Hidden Features and Dark Corners of C++/STL" on comp.lang.c++.moderated, I was completely surprised that it compiled and worked in both Visual Studio 2008 and G++ 4.4. The code:

#include <stdio.h>
int main()
     int x = 10;
     while( x --> 0 ) // x goes to 0
       printf("%d ", x);

Where in the standard is this defined, and where did it come from?

I'd assume C, since it works in GCC as well, but I put C++ on there just in case C++ has more to mention on it. On a more subjective note, I've never heard of this before, had anybody else? Is it worth using?

Answered By: Charles Salvia ( 1680)

That's not an operator -->. That's two separate operators, -- and >.

Your condition code is decrementing x, while returning xs original (not decremented) value, and then comparing the original value with 0 using the > operator.

To better understand, the statement could be as follows:

while( (x--) > 0 )
Partick H&#246;se

I am the developer of some family tree software (written in C++ and Qt). I had no problems until one of my customers mailed me a bug report. The problem is that he has two children with his own daughter, and, as a result, he can't use my software because of errors.

Those errors are the result of my various assertions and invariants about the family graph being processed (for example, after walking a cycle, the program states that X can't be both father and grandfather of Y).

How can I resolve those errors without removing all data assertions?

Answered By: Bert Goethals ( 485)

It seems you (and/or your company) have a fundamental misunderstanding of what a family tree is supposed to be.

Let me clarify, I also work for a company that has (as one of its products) a family tree in its portfolio, and we have been struggling with similar problems.

The problem, in our case, and I assume your case as well, comes from the GEDCOM format that is extremely opinionated about what a family should be. However this format contains some severe misconceptions about what a family tree really looks like.

It contains many issues, such as incompatibility with same sex relations, incest, etc... Which in real life happens more often than you'd imagine (especially when going back in time to the 1700-1800).

We have modeled our family tree to what happens in the real world: Events (for example, births, weddings, engagement, unions, deaths, adoptions, etc.). We do not put any restrictions on these, except for logically impossible ones (for example, one can't be one's own parent, relations need two individuals, etc...)

The lack of validations gives us a more "real world", simpler and more flexible solution.

As for this specific case, I would suggest removing the assertions as they do not hold universally.

For displaying issues (that will arise) I would suggest drawing the same node as many times as needed, hinting at the duplication by lighting up all the copies on selecting one of them.

Suppose a1, b1, c1, and d1 point to heap memory and my numerical code has the following core loop.

const int n=100000

for(int j=0;j<n;j++){
    a1[j] += b1[j];
    c1[j] += d1[j];

This loop is executed 10,000 times via another outer for loop. To speed it up, I changed the code to:

for(int j=0;j<n;j++){
    a1[j] += b1[j];
for(int j=0;j<n;j++){
    c1[j] += d1[j];

Compiled on MS Visual C++ 10.0 with full optimization and SSE2 enabled for 32-bit on a Intel Core 2 Duo (x64), the first example takes 5.5 seconds and the double-loop example takes only 1.9 seconds. My question is: (Please refer to the my rephrased question at the bottom)

PS: I am not sure, if this helps:

Disassembly for the first loop basically looks like this (this block is repeated about five times in the full program):

movsd       xmm0,mmword ptr [edx+18h]
addsd       xmm0,mmword ptr [ecx+20h]
movsd       mmword ptr [ecx+20h],xmm0
movsd       xmm0,mmword ptr [esi+10h]
addsd       xmm0,mmword ptr [eax+30h]
movsd       mmword ptr [eax+30h],xmm0
movsd       xmm0,mmword ptr [edx+20h]
addsd       xmm0,mmword ptr [ecx+28h]
movsd       mmword ptr [ecx+28h],xmm0
movsd       xmm0,mmword ptr [esi+18h]
addsd       xmm0,mmword ptr [eax+38h]

Each loop of the double loop example produces this code (the following block is repeated about three times):

addsd       xmm0,mmword ptr [eax+28h]
movsd       mmword ptr [eax+28h],xmm0
movsd       xmm0,mmword ptr [ecx+20h]
addsd       xmm0,mmword ptr [eax+30h]
movsd       mmword ptr [eax+30h],xmm0
movsd       xmm0,mmword ptr [ecx+28h]
addsd       xmm0,mmword ptr [eax+38h]
movsd       mmword ptr [eax+38h],xmm0
movsd       xmm0,mmword ptr [ecx+30h]
addsd       xmm0,mmword ptr [eax+40h]
movsd       mmword ptr [eax+40h],xmm0

EDIT: The question turned out to be of no relevance, as the behavior severely depends on the sizes of the arrays (n) and the CPU cache. So if there is further interest, I rephrase the question:

Could you provide some solid insight into the details that lead to the different cache behaviors as illustrated by the five regions on the following graph?

It might also be interesting to point out the differences between CPU/cache architectures, by providing a similar graph for these CPUs.

PPS: The full code is at It uses TBB Tick_Count for higher resolution timing, which can be disabled by not defining the TBB_TIMING Macro.

(It shows FLOP/s for different values of n.)

enter image description here

Answered By: Mysticial ( 626)

Upon further analysis of this, I believe this is (at least partially) caused by data alignment of the four pointers. This will cause some level of cache bank/way conflicts.

If I've guessed correctly on how you are allocating your arrays, they are likely to be aligned to the page line.

This means that all your accesses in each loop will fall on the same cache way. However, Intel processors have had 8-way L1 cache associativity for a while. But in reality, the performance isn't completely uniform. Accessing 4-ways is still slower than say 2-ways.

EDIT : It does in fact look like you are allocating all the arrays separately. Usually when such large allocations are requested, the allocator will request fresh pages from the OS. Therefore, there is a high chance that large allocations will appear at the same offset from a page-boundary.

Here's the test code:

int main(){
    const int n = 100000;

    double *a1 = (double*)malloc(n * sizeof(double));
    double *b1 = (double*)malloc(n * sizeof(double));
    double *c1 = (double*)malloc(n * sizeof(double));
    double *d1 = (double*)malloc(n * sizeof(double));
    double *a1 = (double*)malloc(n * sizeof(double) * 4);
    double *b1 = a1 + n;
    double *c1 = b1 + n;
    double *d1 = c1 + n;

    //  Zero the data to prevent any chance of denormals.
    memset(a1,0,n * sizeof(double));
    memset(b1,0,n * sizeof(double));
    memset(c1,0,n * sizeof(double));
    memset(d1,0,n * sizeof(double));

    //  Print the addresses
    cout << a1 << endl;
    cout << b1 << endl;
    cout << c1 << endl;
    cout << d1 << endl;

    clock_t start = clock();

    int c = 0;
    while (c++ < 10000){

        for(int j=0;j<n;j++){
            a1[j] += b1[j];
            c1[j] += d1[j];
        for(int j=0;j<n;j++){
            a1[j] += b1[j];
        for(int j=0;j<n;j++){
            c1[j] += d1[j];


    clock_t end = clock();
    cout << "seconds = " << (double)(end - start) / CLOCKS_PER_SEC << endl;

    return 0;

Benchmark Results:

EDIT: Results on an actual Core 2 architecture machine:

2 x Intel Xeon X5482 Harpertown @ 3.2 GHz:

#define ONE_LOOP
seconds = 6.206

//#define ONE_LOOP
seconds = 2.116

#define ONE_LOOP
seconds = 1.894

//#define ONE_LOOP
seconds = 1.993


  • 6.206 seconds with one loop and 2.116 seconds with two loops. This reproduces the OP's results exactly.

  • In the first two tests, the arrays are allocated separately. You'll notice that they all have the same alignment relative to the page.

  • In the second two tests, the arrays are packed together to break that alignment. Here you'll notice both loops are faster. Furthermore, the second (double) loop is now the slower one as you would normally expect.

As @Stephen Cannon points out in the comments, there is very likely possibility that this alignment causes false aliasing in the load/store units or the cache. I Googled around for this and found that Intel actually has a hardware counter for partial address aliasing stalls:

5 Regions - Explanations

Region 1:

This one is easy. The dataset is so small that the performance is dominated by overhead like looping and branching.

Region 2:

Here, as the data sizes increases, the amount of relative overhead goes down and the performance "saturates". Here two loops is slower because it has twice as much loop and branching overhead.

I'm not sure exactly what's going on here... Alignment could still play an effect as Agner Fog mentions cache bank conflicts. (That link is about Sandy Bridge, but the idea should still be applicable to Core 2.)

Region 3:

At this point, the data no longer fits in L1 cache. So performance is capped by the L1 <-> L2 cache bandwidth.

Region 4:

The performance drop in the single-loop is what we are observing. And as mentioned, this is due to the alignment which (most likely) causes false aliasing stalls in the processor load/store units.

However, in order for false aliasing to occur, there must be a large enough stride between the datasets. This is why you don't see this in region 3.

Region 5:

At this point, nothing fits in cache. So you're bound by memory bandwidth.

2 x Intel X5482 Harpertown @ 3.2 GHz Intel Core i7 870 @ 2.8 GHz Intel Core i7 2600K @ 4.4 GHz

Why does this bit of code,

const float x[16] = {  1.1,   1.2,   1.3,     1.4,   1.5,   1.6,   1.7,   1.8,
                       1.9,   2.0,   2.1,     2.2,   2.3,   2.4,   2.5,   2.6};
const float z[16] = {1.123, 1.234, 1.345, 156.467, 1.578, 1.689, 1.790, 1.812,
                     1.923, 2.034, 2.145,   2.256, 2.367, 2.478, 2.589, 2.690};
float y[16];
for (int i = 0; i < 16; i++)
    y[i] = x[i];

for (int j = 0; j < 9000000; j++)
    for (int i = 0; i < 16; i++)
        y[i] *= x[i];
        y[i] /= z[i];
        y[i] = y[i] + 0.1f; // <--
        y[i] = y[i] - 0.1f; // <--

run more than 10 times faster than the following bit (identical except where noted)?

const float x[16] = {  1.1,   1.2,   1.3,     1.4,   1.5,   1.6,   1.7,   1.8,
                       1.9,   2.0,   2.1,     2.2,   2.3,   2.4,   2.5,   2.6};
const float z[16] = {1.123, 1.234, 1.345, 156.467, 1.578, 1.689, 1.790, 1.812,
                     1.923, 2.034, 2.145,   2.256, 2.367, 2.478, 2.589, 2.690};
float y[16];
for (int i = 0; i < 16; i++)
    y[i] = x[i];

for (int j = 0; j < 9000000; j++)
    for (int i = 0; i < 16; i++)
        y[i] *= x[i];
        y[i] /= z[i];
        y[i] = y[i] + 0; // <--
        y[i] = y[i] - 0; // <--

when compiling with Visual Studio 2010 SP1. (I haven't tested with other compilers.)

Answered By: Mysticial ( 814)

Welcome to the world of denormalized floating-point! They can wreak havoc on performance!!!

Denormal (or subnormal) numbers are kind of a hack to get some extra values very close to zero out of the floating point representation. Operations on denormalized floating-point can be tens to hundreds of times slower than on normalized floating-point. This is because many processors can't handle them directly and must trap and resolve them using microcode.

If you print out the numbers after 10,000 iterations, you will see that they have converged to different values depending on whether 0 or 0.1 is used.

Here's the test code compiled on x64:

int main() {

    double start = omp_get_wtime();

    const float x[16]={1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2.0,2.1,2.2,2.3,2.4,2.5,2.6};
    const float z[16]={1.123,1.234,1.345,156.467,1.578,1.689,1.790,1.812,1.923,2.034,2.145,2.256,2.367,2.478,2.589,2.690};
    float y[16];
    for(int i=0;i<16;i++)
    for(int j=0;j<9000000;j++)
        for(int i=0;i<16;i++)

            if (j > 10000)
                cout << y[i] << "  ";
        if (j > 10000)
            cout << endl;

    double end = omp_get_wtime();
    cout << end - start << endl;

    return 0;


#define FLOATING
1.78814e-007  1.3411e-007  1.04308e-007  0  7.45058e-008  6.70552e-008  6.70552e-008  5.58794e-007  3.05474e-007  2.16067e-007  1.71363e-007  1.49012e-007  1.2666e-007  1.11759e-007  1.04308e-007  1.04308e-007
1.78814e-007  1.3411e-007  1.04308e-007  0  7.45058e-008  6.70552e-008  6.70552e-008  5.58794e-007  3.05474e-007  2.16067e-007  1.71363e-007  1.49012e-007  1.2666e-007  1.11759e-007  1.04308e-007  1.04308e-007

//#define FLOATING
6.30584e-044  3.92364e-044  3.08286e-044  0  1.82169e-044  1.54143e-044  2.10195e-044  2.46842e-029  7.56701e-044  4.06377e-044  3.92364e-044  3.22299e-044  3.08286e-044  2.66247e-044  2.66247e-044  2.24208e-044
6.30584e-044  3.92364e-044  3.08286e-044  0  1.82169e-044  1.54143e-044  2.10195e-044  2.45208e-029  7.56701e-044  4.06377e-044  3.92364e-044  3.22299e-044  3.08286e-044  2.66247e-044  2.66247e-044  2.24208e-044

Note how in the second run the numbers are very close to zero.

Denormalized numbers are generally rare and thus most processors don't try to handle them efficiently.

To demonstrate that this has everything to do with denormalized numbers, if we flush denormals to zero by adding this to the start of the code:


Then the version with 0 is no longer 10x slower and actually becomes faster. (This requires that the code be compiled with SSE enabled.)

This means that rather than using these weird lower precision almost-zero values, we just round to zero instead.

Timings: Core i7 920 @ 3.5 GHz:

//  Don't flush denormals to zero.
0.1f: 0.564067
0   : 26.7669

//  Flush denormals to zero.
0.1f: 0.587117
0   : 0.341406

In the end, this really has nothing to do with whether it's an integer or floating-point. The 0 or 0.1f is converted/stored into a register outside of both loops. So that has no effect on performance.


What's the most elegant way to split a string in C++? The string can be assumed to be composed of words separated by whitespace.

(Note that I'm not interested in C string functions or that kind of character manipulation/access. Also, please give precedence to elegance over efficiency in your answer.)

The best solution I have right now is:

#include <iostream>
#include <sstream>
#include <string>
using namespace std;

int main()
    string s("Somewhere down the road");
    istringstream iss(s);

        string sub;
        iss >> sub;
        cout << "Substring: " << sub << endl;
    } while (iss);

Answered By: Evan Teran ( 442)

I use this to split string by a delim. The first puts the results in an pre-constructed vector, the second returns a new vector.

std::vector<std::string> &split(const std::string &s, char delim, std::vector<std::string> &elems) {
    std::stringstream ss(s);
    std::string item;
    while (std::getline(ss, item, delim)) {
    return elems;

std::vector<std::string> split(const std::string &s, char delim) {
    std::vector<std::string> elems;
    return split(s, delim, elems);

EDIT: Note that this solution does not skip empty tokens, so the following will find 4 items, one of which is empty:

std::vector<std::string> x = split("one:two::three", ':');
Viniyo Shouta

I'm reading a book where the author says that if( a < 901 ) is faster than if( a <= 900 ).

Not exactly as in this simple example, but there are slight performance changes on loop complex code. I suppose this has to do something with generated machine code in case it's even true.

Answered By: Jonathon Reinhart ( 910)

No, it will not be faster on most architectures. You didn't specify, but on x86, all of the integral comparisons will be typically implemented in two machine instructions:

  • A test or cmp instruction, which sets EFLAGS
  • And a Jcc (jump) instruction, depending on the comparison type (and code layout):
    • jne - Jump if not equal --> ZF = 0
    • jz - Jump if zero (equal) --> ZF = 1
    • jg - Jump if greater --> ZF = 0 and SF = OF
    • (etc...)

Example (Edited for brevity) Compiled with $ gcc -m32 -S -masm=intel test.c

    if (a < b) {
        // Do something 1

Compiles to:

    mov     eax, DWORD PTR [esp+24]      ; a
    cmp     eax, DWORD PTR [esp+28]      ; b
    jge     .L2                          ; jump if a is >= b
    ; Do something 1


    if (a <= b) {
        // Do something 2

Compiles to:

    mov     eax, DWORD PTR [esp+24]      ; a
    cmp     eax, DWORD PTR [esp+28]      ; b
    jg      .L5                          ; jump if a is > b
    ; Do something 2

So the only difference between the two is a jg versus a jge instruction. The two will take the same amount of time.

I'd like to address the comment that nothing indicates that the different jump instructions take the same amount of time. This one is a little tricky to answer, but here's what I can give: In the Intel Instruction Set Reference, they are all grouped together under one common instruction, Jcc (Jump if condition is met). The same grouping is made together under the Optimization Reference Manual, in Appendix C. Latency and Throughput.

Latency — The number of clock cycles that are required for the execution core to complete the execution of all of the μops that form an instruction.

Throughput — The number of clock cycles required to wait before the issue ports are free to accept the same instruction again. For many instructions, the throughput of an instruction can be significantly less than its latency

The values for Jcc are:

      Latency   Throughput
Jcc     N/A        0.5

with the following footnote on Jcc:

7) Selection of conditional jump instructions should be based on the recommendation of section Section 3.4.1, “Branch Prediction Optimization,” to improve the predictability of branches. When branches are predicted successfully, the latency of jcc is effectively zero.

So, nothing in the Intel docs ever treats one Jcc instruction any differently from the others.

If one thinks about the actual circuitry used to implement the instructions, one can assume that there would be simple AND/OR gates on the different bits in EFLAGS, to determine whether the conditions are met. There is then, no reason that an instruction testing two bits should take any more or less time than one testing only one (Ignoring gate propagation delay, which is much less than the clock period.)

Edit: Floating Point

This holds true for x87 floating point as well: (Pretty much same code as above, but with double instead of int.)

        fld     QWORD PTR [esp+32]
        fld     QWORD PTR [esp+40]
        fucomip st, st(1)              ; Compare ST(0) and ST(1), and set CF, PF, ZF in EFLAGS
        fstp    st(0)
        seta    al                     ; Set al if above (CF=0 and ZF=0).
        test    al, al
        je      .L2
        ; Do something 1

        fld     QWORD PTR [esp+32]
        fld     QWORD PTR [esp+40]
        fucomip st, st(1)              ; (same thing as above)
        fstp    st(0)
        setae   al                     ; Set al if above or equal (CF=0).
        test    al, al
        je      .L5
        ; Do something 2
James McNellis

I am having some difficulty compiling a C++ program that I've written.

This program is very simple and, to the best of my knowledge, conforms to all the rules set forth in the C++ Standard. I've read over the entirety of ISO/IEC 14882:2003 twice to be sure.

The program is as follows:

enter image description here

Here is the output I received when trying to compile this program with Visual C++ 2010:

c:\dev>cl /nologo helloworld.png
cl : Command line warning D9024 : unrecognized source file type 'helloworld.png', object file assumed
helloworld.png : fatal error LNK1107: invalid or corrupt file: cannot read at 0x5172

Dismayed, I tried g++ 4.5.2, but it was equally unhelpful:

c:\dev>g++ helloworld.png
helloworld.png: file not recognized: File format not recognized
collect2: ld returned 1 exit status

I figured that Clang (version 3.0 trunk 127530) must work, since it is so highly praised for its standards conformance. Unfortunately, it didn't even give me one of its pretty, highlighted error messages:

c:\dev>clang++ helloworld.png
helloworld.png: file not recognized: File format not recognized
collect2: ld returned 1 exit status
clang++: error: linker (via gcc) command failed with exit code 1 (use -v to see invocation)

To be honest, I don't really know what any of these error message mean.

Many other C++ programs have source files with a .cpp extension, so I thought perhaps I needed to rename my file. I changed its name to helloworld.cpp, but that didn't help. I think there is a very serious bug in Clang because when I tried using it to compile the renamed program, it flipped out, printed "84 warnings and 20 errors generated." and made my computer beep a lot!

What have I done wrong here? Have I missed some critical part of the C++ Standard? Or are all three compilers really just so broken that they can't compile this simple program?

Answered By: Sven ( 577)

Originally from Overv @ reddit.

Gabriel Isenberg

I have a C++ application I'm in the process of optimizing. What tool can I use to pinpoint my slow code?

Answered By: Mike Dunlavey ( 522)

If your goal is to use a profiler, use one of the suggested ones.

However, if you're in a hurry and you can manually interrupt your program under the debugger while it's being subjectively slow, there's a simple way to find performance problems.

Just halt it several times, and each time look at the call stack. If there is some code that is wasting some percentage of the time, 20% or 50% or whatever, that is the probability that you will catch it in the act on each sample. So that is roughly the percentage of samples on which you will see it. There is no educated guesswork required. If you do have a guess as to what the problem is, this will prove or disprove it.

You may have multiple performance problems of different sizes. If you clean out any one of them, the remaining ones will take a larger percentage, and be easier to spot, on subsequent passes.

Caveat: programmers tend to be skeptical of this technique unless they've used it themselves. They will say that profilers give you this information, but that is only true if they sample the entire call stack. Call graphs don't give you the same information, because 1) they don't summarize at the instruction level, and 2) they give confusing summaries in the presence of recursion. They will also say it only works on toy programs, when actually it works on any program, and it seems to work better on bigger programs, because they tend to have more problems to find.

P.S. This can also be done on multi-thread programs if there is a way to collect call-stack samples of the thread pool at a point in time, as there is in Java.

P.P.S As a rough generality, the more layers of abstraction you have in your software, the more likely you are to find that that is the cause of performance problems (and the opportunity to get speedup).

Added: It might not be obvious, but the stack sampling technique works equally well in the presence of recursion. The reason is that the time that would be saved by removal of an instruction is approximated by the fraction of samples containing it, regardless of the number of times it may occur within a sample.

Another objection I often hear is: "It will stop someplace random, and it will miss the real problem". This comes from having a prior concept of what the real problem is. A key property of performance problems is that they defy expectations. Sampling tells you something is a problem, and your first reaction is disbelief. That is natural, but you can be sure if it finds a problem it is real, and vice-versa.

ADDED: Let me make a Bayesian explanation of how it works. Suppose there is some instruction I (call or otherwise) which is on the call stack some fraction f of the time (and thus costs that much). For simplicity, suppose we don't know what f is, but assume it is either 0.1, 0.2, 0.3, ... 0.9, 1.0, and the prior probability of each of these possibilities is 0.1, so all of these costs are equally likely a-priori.

Then suppose we take just 2 stack samples, and we see instruction I on both samples, designated observation o=2/2. This gives us new estimates of the frequency f of I, according to this:

P(f=x) x  P(o=2/2|f=x) P(o=2/2&&f=x)  P(o=2/2&&f >= x)  P(f >= x)

0.1    1     1             0.1          0.1            0.25974026
0.1    0.9   0.81          0.081        0.181          0.47012987
0.1    0.8   0.64          0.064        0.245          0.636363636
0.1    0.7   0.49          0.049        0.294          0.763636364
0.1    0.6   0.36          0.036        0.33           0.857142857
0.1    0.5   0.25          0.025        0.355          0.922077922
0.1    0.4   0.16          0.016        0.371          0.963636364
0.1    0.3   0.09          0.009        0.38           0.987012987
0.1    0.2   0.04          0.004        0.384          0.997402597
0.1    0.1   0.01          0.001        0.385          1

                  P(o=2/2) 0.385                

The last column says that, for example, the probability that f >= 0.5 is 92%, up from the prior assumption of 60%.

Suppose the prior assumptions are different. Suppose we assume P(f=0.1) is .991 (nearly certain), and all the other possibilities are almost impossible (0.001). In other words, our prior certainty is that I is cheap. Then we get:

P(f=x) x  P(o=2/2|f=x) P(o=2/2&& f=x)  P(o=2/2&&f >= x)  P(f >= x)

0.001  1    1              0.001        0.001          0.072727273
0.001  0.9  0.81           0.00081      0.00181        0.131636364
0.001  0.8  0.64           0.00064      0.00245        0.178181818
0.001  0.7  0.49           0.00049      0.00294        0.213818182
0.001  0.6  0.36           0.00036      0.0033         0.24
0.001  0.5  0.25           0.00025      0.00355        0.258181818
0.001  0.4  0.16           0.00016      0.00371        0.269818182
0.001  0.3  0.09           0.00009      0.0038         0.276363636
0.001  0.2  0.04           0.00004      0.00384        0.279272727
0.991  0.1  0.01           0.00991      0.01375        1

                  P(o=2/2) 0.01375                

Now it says P(f >= 0.5) is 26%, up from the prior assumption of 0.6%. So Bayes allows us to update our estimate of the probable cost of I. If the amount of data is small, it doesn't tell us accurately what the cost is, only that it is big enough to be worth fixing.

Yet another way to look at it is called the Rule Of Succession. If you flip a coin 2 times, and it comes up heads both times, what does that tell you about the probable weighting of the coin? The respected way to answer is to say that it's a Beta distribution, with average value (number of hits + 1) / (number of tries + 2) = (2+1)/(2+2) = 75%.

(The key is that we see I more than once. If we only see it once, that doesn't tell us much except that f > 0.)

So, even a very small number of samples can tell us a lot about the cost of instructions that it sees. (And it will see them with a frequency, on average, proportional to their cost. If n samples are taken, and f is the cost, then I will appear on nf+/-sqrt(nf(1-f)) samples. Example, n=10, f=0.3, that is 3+/-1.4 samples.)