Need for speed (2022)

Because robots need performant software

Why speed is important

  • Throughput: can you process your sensor data in real time?

  • Latency: how long does it take to react?

  • Closed-loop control: how fast is your loop?

  • Bill of Material: how powerful does your hardware need to be?

"Can I run my algorithm on a GPU?"

CPP Optimizations Diary

  • Simple and actionable rules to find bottlenecks in your code.
  • Best practices to avoid "death by 1000 paper cuts".
  • Practical examples from my Open Source contributions.

"That feeling when your software runs 2x faster"

Don't make assumptions,  measure first.

Learn performance-related design patterns.

Don't sacrifice code quality and readability

Best profiling tool: PERF

  • Contrariwise to Callgrind/Valgrind, it has a negligible overhead.
  • You can attach to a running process (desirable in ROS).
  • Important: its measurement is based on sampling, therefore it is not fully deterministic.
  • You should compile your project with RelWithDebInfo

Perf and Hotspot

  1. Whole-application benchmarking? Hard to find the "smoking gun".
  2. Intrusive profiling like easy_profiler? So 2000s !
  3. Embrace Linux Perf and Hotspot !

Tools: Google Benchmark


Tools: Heaptrack

  • Heaptrack is better than Valgrind!
  • Nice GUI!


Tip #1: avoid memory allocations

  • Memory allocation can be expensive.
  • Perf can tell you how much CPU is wasted doing new and delete
  • Beware data structure that will create objects in the heap (std::list and std::map, during insertion).
  • Allocating "big" objects might be slow (PointClouds, Images, etc.). Consider "recycling" objects or use a memory pool.

Memory pools you might want to check:



Tip #2: use std::vector and SmallVector

Use std::vector<>::reserve()

std::vector<size_t> v;
for(size_t i=0; i<100; i++) 

Tip #2: use std::vector and SmallVector

Example: improving The RealSense ROS driver

// BEFORE: this list was created and populated at each frame
std::list<unsigned> valid_indices;

// AFTER: create this vector only once and re-use it (after clean)
// The memory is allocate only once and iteration is faster
std::vector<unsigned> valid_indices;

Tip #2: use std::vector and SmallVector

SmallVector: a data structure that pre-allocates a certain number of elements in the stack (not the heap).




Avoid std::list, prefer std::deque or boost::circular_buffer

Tip #3: test multiple associative containers

  • std::map is usually bad
  • std::unordered_map is a good default.
  • Sometimes, an ordered std::vector<std::pair<Key,Value>> is an option.
  • Consider  boost::container_flat_map.
  • There are many alternatives to unordered_map which claim to be faster.

Tip #3: test multiple associative containers

Change: remove std::map and use std::vector instead

Tip #4: don't compute twice

std::vector<double> LUT_cos;
std::vector<double> LUT_sin;

double angle = angle_minimum;

for(int i=0; i<scan_distance.size(); i++)
    LUT_cos.push_back( cos(angle) );
    LUT_sin.push_back( sin(angle) );
    angle += angle_increment;

// ----- The efficient scan conversion ------
std::vector<double> scan_distance;
std::vector<Pos2D> cartesian_points;

cartesian_points.reserve( scan_distance.size() );

for(int i=0; i<scan_distance.size(); i++)
    const double dist = scan_distance[i];
    double x = dist*LUT_cos[i];
    double y = dist*LUT_sin[i];
    cartesian_points.push_back( Pos2D(x,y) );

Example: polar to cartesian transform in LaserScan

Tip #4: don't compute twice

Tip #4: don't compute twice

// index in a matrix is usally calculated as: 
index = column * num_rows + row;

// Nice and readable...
for( size_t y = y_min; y < y_max; y++ ) 
    for( size_t x = x_min; x < x_max; x++ ) 
        matrix_out( x,y ) = std::max( mat_a( x,y ), mat_b( x,y ) ); 

// ...But considerably faster
for(size_t y = y_min; y < y_max; y++) 
    size_t offset_out =  y * matrix_out.rows();
    size_t offset_a   =  y * mat_a.rows();
    size_t offset_b   =  y * mat_b.rows();
    for(size_t x = x_min; x < x_max; x++) 
        size_t index_out =  offset_out + x;
        size_t index_a   =  offset_a + x;
        size_t index_b   =  offset_b + x;
        matrix_out( index_out ) = std::max( mat_a( index_a ), mat_b( index_b ) ); 

Optimization opportunity iterating over a large 2D matrix.

Tip #5: try changing algorithm

  • Big O() complexity is still the most important factor
  • Explore alternative implementations of the same algorithm


Simply used


instead of



  • No rocket science. Optimizations opportunities are banally simple sometimes.
  • It is all using good tools agressively.

Advanced topics for curious minds

  •  Cache friendly and data-driven development
  • SIMD operations
  • And of course... GPU!

Need for speed (2022)

By Davide Faconti

Need for speed (2022)

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