oneTBB quickstart

Purpose

Parallelizes C++ loops and reductions with Intel oneAPI Threading Building Blocks on Intel CPUs: the umbrella header, parallel_for and parallel_reduce over a blocked_range, the CMake lines that link it, and the assumptions the task-based model breaks.

Prefer oneTBB over hand-rolled threads when the work is a bounded loop or a reduction over a container: the partitioner decides the split, and the runtime composes with other oneTBB-based libraries in the same process instead of oversubscribing the machine.

When to Use This Skill

Use this skill when:

• A C++ loop or reduction is a candidate for multithreading.
• A first oneTBB program needs headers, namespace, and CMake wiring.
• A parallel_for body has a race on shared state.
• A reduction accumulates into one variable from many threads.

Do not use this skill for GPU offload, for OpenMP or std::thread questions, or for tuning an existing oneTBB program's grain size and partitioners — that is past "getting started" and belongs with a profile in hand.

Quick Start

#include <oneapi/tbb.h>            // umbrella header

int main() {
    oneapi::tbb::parallel_for(
        oneapi::tbb::blocked_range<size_t>(0, n),
        [&](const oneapi::tbb::blocked_range<size_t>& r) {
            for (size_t i = r.begin(); i != r.end(); ++i)
                out[i] = f(in[i]);
        });
}

using namespace oneapi::tbb; shortens the calls; qualifying them keeps the origin visible in code that mixes threading libraries.

Implementation Guide

1. Parallelize a loop with parallel_for over a range. The body receives a subrange, not a single index — iterate inside it, as above. The range type carries the index type, so blocked_range<size_t> and blocked_range<int> are different instantiations.

2. Reduce with parallel_reduce, not a shared accumulator. The body folds a subrange into a partial result and the last argument combines two partials:

   double sum = oneapi::tbb::parallel_reduce(
       oneapi::tbb::blocked_range<size_t>(0, n), 0.0,
       [&](const auto& r, double acc) {
           for (size_t i = r.begin(); i != r.end(); ++i) acc += a[i];
           return acc;
       },
       std::plus<double>());
   

3. Link it in CMake. oneTBB ships a package config, so the two lines are the whole build change:

   find_package(TBB REQUIRED)
   target_link_libraries(my_app PRIVATE TBB::tbb)
   

4. Leave the grain size alone at first. The auto-partitioner chooses the split; a hand-set grain size is a tuning decision that needs a measurement behind it, and a wrong one is worse than none.

5. Make shared state safe or remove it. If the body must write to a shared structure, use one of the concurrent_* containers or restructure as a reduction. A mutex around the body of a parallel_for usually gives back the parallelism it was added to protect.

Performance

No measured numbers ship with this skill, and a parallel version is not automatically a faster one. What to measure:

• Compare against the serial loop on the same input, with the same compiler flags and optimization level.
• Watch for a body too small to cover the task overhead: at that size the partitioner's fixed cost shows up as a slowdown.
• Check whether the loop is memory-bandwidth bound before adding threads — more threads on a saturated bus do not help.
• Count the threads in the process. Nested parallelism from another library, or an OpenMP region around a oneTBB call, oversubscribes the cores and the slowdown is not in either loop.

Gotchas & Limitations

• The body runs many times, concurrently. It is not called once per loop and not once per thread; the range is split as the runtime sees fit. Anything captured by reference and written to is shared mutable state.
• parallel_reduce is not deterministic in floating point. The combination order varies between runs, so sums can differ in the last bits. Use parallel_deterministic_reduce when a reproducible result matters more than speed.
• Exceptions propagate out of the algorithm, not out of the body where they were thrown — one is rethrown on the caller's thread and the rest are lost.
• find_package(TBB) needs oneTBB's own config, which the environment script or the package install puts on CMAKE_PREFIX_PATH. A build that cannot find it usually has not sourced the environment.
• Not covered: flow graph, task groups, arenas and thread affinity, and the deprecated tbb:: (pre-oneAPI) spellings.

References

File	Load it when
references/official-sources.md	you need the current oneTBB API for an algorithm, the deprecation status of a tbb:: name, or the supported CMake integration for the installed version

Two things here should not be answered from memory: the current name and signature of an algorithm (oneTBB renamed and dropped parts of the pre-oneAPI API, and the old spellings still compile in some builds) and how the package is found by CMake in the installed layout.