Modern C++ embraces the reality of multicore processors by providing high-level tools for parallelism. The C++17 standard introduced parallel algorithms, essentially parallelised versions of existing STL algorithms, to help programs take advantage of multiple cores for improved performance. In this post, we explore how C++17 parallel algorithms work, focusing on the execution policies (std::execution::seq, par, and par_unseq), with examples of their use. We also discuss the performance benefits of parallel execution and critically examine the limitations – including overhead, portability, and scenarios where parallelism may not pay off. The tone is analytical and formal, with a viewpoint that whilst parallelism is powerful, it must be applied judiciously.

Execution Policies in C++17 Standard Algorithms

Parallel algorithms in C++17 are enabled by execution policies, defined in the <execution> header. These policy objects are passed as the first argument to a standard algorithm call, indicating how the algorithm is allowed to execute. C++17 defines three such policies:

  • std::execution::seq (sequenced) – Execute the algorithm strictly sequentially (no parallelism). This is equivalent to the usual serial execution and is the default if no policy is specified.
  • std::execution::par (parallel) – Allow the algorithm to execute in parallel across threads (multiple threads may divide the work). However, execution within each thread remains sequential (no inter-thread vectorisation).
  • std::execution::par_unseq (parallel unsequenced) – Allow full parallelism: multiple threads and unsequenced execution (vectorisation) within threads. This gives the implementation freedom to reorder and vectorise operations in addition to using multiple threads. (For completeness, C++20 later added std::execution::unseq for vectorisation without multithreading, but we will focus on the C++17 policies.)

Using an execution policy is straightforward: you simply include <execution> and pass the policy to the algorithm call. The library provides overloads of many standard algorithms that take an ExecutionPolicy parameter. The only change from a normal call is this extra first argument, which makes it easy to upgrade a sequential algorithm to run in parallel. For example, here we use std::transform to square a range of numbers in parallel:

#include <vector>
#include <execution>
#include <algorithm>

// ... (initialize a std::vector<int> data with values)
std::vector<int> result(data.size());

// Parallel transform: square each element in data, store in result.
std::transform(std::execution::par_unseq,
               data.begin(), data.end(),
               result.begin(),
               [](int x) { return x * x; });

In this snippet, the only difference from a traditional std::transform call is the std::execution::par_unseq policy specified as the first argument. This hints to the library that it may perform the transformation in parallel and with vectorised instructions, dividing work among threads as appropriate. The code remains clear and declarative – we express what to do (square each element) while the library decides how to do it in parallel. Similarly, we can sort a large container with minimal changes to the call:

std::vector<double> bigData = /* ... */;
// Sequential sort:
std::sort(bigData.begin(), bigData.end());
// Parallel sort using multiple threads:
std::sort(std::execution::par, bigData.begin(), bigData.end());

By simply adding std::execution::par, the sorting algorithm is permitted to utilise multiple threads to sort the data, which can greatly accelerate sorting of big datasets (e.g. millions of elements) without any further code changes. If no execution policy is given, the call defaults to the sequenced (seq) behavior, so explicitly using seq is mainly for clarity or to force sequential execution when desired.

Algorithm support: Most standard algorithms that make sense to parallelise have overloads for execution policies. This includes algorithms like std::for_each, std::transform, std::copy, sorting algorithms (std::sort, std::stable_sort), partitioning and searching algorithms, and new algorithms introduced in C++17 such as std::reduce (a parallel-friendly alternative to std::accumulate). Not every algorithm is parallelised (for example, std::accumulate itself remains sequential to guarantee left-to-right order, whereas std::reduce allows arbitrary grouping for parallelism), but the library covers a broad set of common computations with parallel counterparts.

Requirements and safety: It is the programmer’s responsibility to ensure that the operation used in a parallel algorithm is safe for concurrent execution. In practice, this means the functor or lambda you pass should not introduce data races – e.g. writing to shared variables without synchronisation – and should not rely on element processing order. Under par policy, invocations run concurrently on different threads, so they must obey the usual rules of concurrent code (multiple readers or one writer per memory location, no unsynchronised data sharing). Under par_unseq, the requirements are even stricter: the callable must be vectorisation-safe. In other words, it cannot perform operations that inherently require a strict sequence or mutual exclusion, such as acquiring locks or other synchronisation primitives. If code uses a mutex or other blocking synchronization inside a par_unseq algorithm, it could lead to deadlocks or undefined behavior, since the policy permits the implementation to interleave operations without the usual sequential ordering. Rule of thumb: if your functor would cause data races when executed in parallel, stick to seq (or fix the data sharing); if it uses something like locks or I/O that cannot be safely vectorised, prefer par over par_unseq. When in doubt, using the par policy is safer, as it forbids certain aggressive optimisations but still allows multi-threading.

Finally, note that exceptions thrown inside parallel algorithms are not propagated in the usual way. The C++17 standard dictates that if a user-provided operation invoked by a standard algorithm with a parallel policy throws an exception, the program will call std::terminate (effectively aborting). This design avoids complex error-handling across threads, but it means you should avoid throwing exceptions from within the algorithm’s functors. Any needed error handling should be done by other means (e.g. use atomic flags or pre-validation of data) because a thrown exception will end the whole program when using par/par_unseq.

Advantages of Using C++17 Parallel Algorithms

Using parallel execution policies can significantly improve performance in compute-intensive scenarios by utilising the full power of modern CPUs. Instead of being limited to a single thread, algorithms like sort, transform, or reduce can engage multiple cores and even vector units, potentially executing many operations simultaneously. This data-parallel approach can yield near-linear speedups relative to sequential execution, in ideal cases, as workload is divided among threads. The standard library’s goal for adding parallel algorithms was exactly to enable such performance gains with minimal effort. Developers no longer need to rewrite algorithms with low-level threading primitives; you can achieve parallel execution by tweaking one line of code. This high-level approach also lets the library apply optimisations automatically. For example, with the par_unseq policy, an implementation might utilise SIMD instructions (vectorising operations on contiguous data) and even adjust workload distribution dynamically (work-stealing scheduling) to maximise throughput. These optimisations happen behind the scenes, potentially improving performance beyond what a naïve manual threading might accomplish, especially for algorithms that can be efficiently vectorised (such as element-wise transformations).

Another major benefit is ease of use and integration. Parallel algorithms are designed to be a drop-in replacement for their sequential counterparts: the code using them looks virtually identical to the original. As demonstrated earlier, converting a serial algorithm call to a parallel one is as simple as adding an execution policy parameter. This preserves code clarity and maintainability – the algorithm’s intent and structure remain explicit, and one does not have to refactor logic into low-level thread management code. It lowers the barrier to entry for parallel programming; developers can leverage multicore hardware without delving into threads, mutexes, and condition variables. In a sense, the execution policy approach is declarative: you declare that an algorithm may run in parallel, and the library takes care of how. This can lead to fewer bugs than hand-written threading, since the library implementation handles the tricky parts of splitting work and synchronising results. The consistency of using standard algorithms also means you are using well-tested components, and your code remains portable standard C++.

Because this functionality is part of the ISO C++ standard, it is (in principle) portable across compilers and platforms. A code using std::for_each(std::execution::par, ...) should compile and run with parallelism on any conforming C++17 implementation that supports the feature. There is no dependency on proprietary frameworks or non-standard extensions – you express parallelism in pure ISO C++, which is important for longevity and portability of code (assuming support is present on the target platform). In practice, major compilers have caught up: modern GCC, Clang, and MSVC all support C++17 parallel algorithms (GCC since roughly version 10, Clang since version 11, and MSVC since VS 2017 updates). This broad support means developers can increasingly rely on these features in production code across different environments.

Finally, parallel algorithms often compose cleanly with other standard library features. For example, one can generate data, sort it in parallel, then use std::reduce to compute a result, all using the STL interface. This promotes an algorithmic style of programming where performance-critical loops are handled by the library. The result is usually both concise and efficient, leveraging well-understood idioms instead of custom loop-and-thread code.

Limitations and When Not to Use Parallel Policies

Despite their appeal, C++17 parallel algorithms are not a magic solution and come with important limitations. Developers should consider the following caveats and scenarios where using par or par_unseq may be counterproductive:

  • Overhead and Small Tasks: Parallelism introduces overhead – spawning threads, synchronising their results, and managing workloads all incur costs. If the amount of work in the algorithm is small (for example, iterating over a short range or doing a very cheap operation per element), these overheads can outweigh the benefits. In such cases, a parallel algorithm might even run slower than the sequential version. As a rule, you need a sufficiently large N or expensive computations to amortise the parallel overhead. Amdahl’s Law reminds us that the speedup from parallelism is limited by any remaining serial portion and by coordination costs. Empirically, it’s known that for very small loops or trivial operations, the sequential execution will likely be fastest. The standard guidance is to benchmark and ensure the parallel version is actually an improvement. If the loop’s total work is minimal, or if you only have a few elements, stick with seq – parallelism “does not come for free”.

  • Contention and False Sharing: Related to overhead, if the algorithm’s work involves contended resources, parallelising can backfire. For example, if each iteration of a loop writes to the same global variable or performs I/O to a single device (like printing to console or writing to one file), multiple threads will contend and serialize on that resource. The Microsoft C++ team notes that additional parallelism can create contention on external resources like disk I/O, limiting any speed gains. Similarly, parallel threads operating on data that lies close in memory can suffer performance degradation due to false sharing (cache contention). In such scenarios, the theoretical parallel speedup is nullified by the bottleneck, and using a sequential approach (or redesigning the workload to avoid contention) is preferable.

  • Non-random-access Iterators: C++17 parallel algorithms are most effective with random-access iterators (e.g. arrays, vectors) where the number of elements is known and they can be evenly partitioned. If you attempt to use them with linked lists or other non-contiguous sequences, the benefits may be limited or nonexistent. Some implementations may even refrain from parallelising algorithms on linked lists because the overhead of chasing pointers in multiple threads can outweigh any gains. The MSVC documentation cautions that parallelisation is “not always faster, particularly for non-random-access iterators”. In other words, if an algorithm is going to end up doing serial work anyway (like traversing a list node by node), adding threads doesn’t help. Know your data structures – a parallel algorithm on a std::list is unlikely to speed things up, whereas on a std::vector it very well might.

  • Thread Safety and Correctness: The introduction of execution policies does not free you from reasoning about thread safety. The library does not automatically make your operations safe – it only provides the framework for parallel execution. If your functor or predicate writes to shared data or modifies the range in unintended ways, you can still have data races and undefined behavior. For example, doing a std::for_each(std::execution::par, vec.begin(), vec.end(), f) where f appends to the same vector will wreak havoc. The onus is on the developer to ensure the work done per element is independent or properly synchronised. In many cases, dependencies in the computation will force sequential execution for correctness. In such cases, you have no choice but to use seq. As noted earlier, even using locks inside a par_unseq algorithm is disallowed – it can lead to deadlock because the execution order is unsequenced. In summary, correctness trumps speed: you should prefer sequential execution (or redesign the algorithm) if parallel execution would introduce races or inconsistent results.

  • Debugging Difficulty: Parallel algorithms can be harder to debug than their sequential counterparts. When you add an execution policy, the execution order becomes non-deterministic (especially with par_unseq). This can complicate debugging because breakpoints or logging statements may behave differently, and race conditions might cause intermittent failures. Also, using standard algorithms means the actual looping is happening inside the library – you can’t easily step through each iteration in a debugger. If you suspect a logic bug inside the operation, it might be simpler to run it sequentially first. The simplicity of a sequential execution is often helpful for initial implementation and debugging. Only once it’s correct should you consider parallelising the code. This is in line with a common approach: get it right with seq, then optimise with par if needed. Furthermore, tools for debugging multi-threaded execution (like thread sanitizers) might not directly trace into the internals of parallel STL algorithms, so figuring out concurrency issues can be tricky. Be prepared for this added complexity when opting for par or par_unseq.

  • Exception Handling and Termination: As mentioned, if something goes wrong inside a parallel algorithm (e.g. an exception is thrown), the program will call std::terminate. This behaviour is by design, but it means you lose the ability to catch and handle exceptions at the call site. This is a limitation to be mindful of – for instance, if your functor performs an operation that might throw (say, converting a string to number, which could throw on bad input), you won’t be able to catch that exception normally. In a sequential algorithm, you could catch exceptions around the loop; in a parallel algorithm, you cannot (the program will simply terminate unless you have a global terminate handler). Thus, any potentially throwing operation should either be avoided or wrapped in a try/catch inside the functor itself (catching internally and handling error by other means). The lack of graceful error propagation is a trade-off to simplify implementations, but it’s a semantic difference from sequential code that can surprise the unwary.

  • Portability and Implementation Variance: Although parallel algorithms are standardized, the standard deliberately does not mandate how they are implemented. This means that the performance characteristics – and even the availability of true parallelism – can vary between library implementations. In the early days of C++17, not all standard libraries provided an implementation of parallel execution policies; for example, GCC’s libstdc++ did not have support until version 9/10 and required certain builds, and Clang’s libc++ followed later. Even today, an implementation is allowed to fall back to sequential execution if it cannot parallelise a given call. In fact, a poorly optimised implementation might use a thread pool or task scheduler that doesn’t scale well, yielding less speedup than expected. There are also differences in how par_unseq is utilised: some standard libraries (such as MSVC’s as of C++17) implemented par and par_unseq in the same way internally, meaning no additional vectorisation benefits were realised. In contrast, other compilers or special platforms might take advantage of par_unseq to enable explicit SIMD instructions. The bottom line is that performance is not uniform across platforms – you should test on the platforms you care about. Portability here also extends to hardware: the execution policies are meant to be general enough to allow execution on GPUs or other accelerators, but using them doesn’t magically offload computation to a GPU unless you are using a toolchain that supports that (for example, some compilers like NVCC or special libraries could map par to GPU kernels). For typical CPU use, ensure that your target compiler+library actually supports the parallel algorithms and that any required support libraries (like threading backends) are present. If not, your code may compile but always run sequentially, or fail to compile altogether.

  • Compilation Overhead: A lesser-known drawback is that including <execution> and using parallel algorithms can increase compile times and binary size. The parallel algorithms feature is template-heavy and may pull in concurrency support code under the hood. Lucian Teodorescu notes that parallel algorithms can add significant compile-time overhead in some cases. This might matter in large projects or for developers concerned with compilation performance. It’s not a show-stopper, but worth noting if you observe a slowdown in build times after adopting parallel algorithms.

Conclusion

Parallel algorithms in C++17 provide a powerful, high-level mechanism to exploit concurrency directly within the standard library. The ability to simply apply std::execution::par or par_unseq to an existing algorithm call and potentially gain a substantial speedup is a notable advancement in the language’s capabilities. In the author’s view, this feature embodies the modern C++ approach: embracing efficiency while preserving abstraction. We no longer have to drop down to low-level threads for many common data-processing tasks; instead, we express what we want done, and let the library decide how to do it concurrently. This leads to code that is both efficient and expressive.

That said, parallel algorithms are not a silver bullet. As we’ve analysed, one must apply them with prudence. Not every problem will benefit from parallel execution, and in some cases it can be detrimental. It remains crucial to measure performance and ensure that parallelism is actually helping for your particular workload and environment. A thoughtful developer will consider factors like data size, algorithm complexity, and hardware topology before blindly parallelising every algorithm call. In essence, C++17’s parallel execution policies give us a powerful tool in the toolbox, but it’s up to us to use the right tool for the job. Used appropriately, they can greatly improve application performance on modern CPUs without sacrificing code clarity. Used inappropriately, they can introduce complexity and overhead for little gain. The arrival of parallel algorithms in the standard is a positive step that moves C++ in the direction of higher-level concurrency, but it does not absolve us from thinking carefully about when and how to leverage parallelism.

In summary, C++17 parallel algorithms enable elegant and potentially fast solutions by combining familiar STL patterns with the power of multicore execution. By understanding the execution policies and their constraints, we can write code that is both clean and scalable. Just remember that with great power comes great responsibility: profile, test, and ensure correctness when unleashing parallel execution in your programs. With that approach, the parallel STL can be a robust ally in building high-performance C++ software.

References:

  1. ISO C++ Standard (2017) – Sections [algorithms.parallel] (Parallel algorithms)
  2. Cppreference – “Execution policy” (online reference)
  3. Microsoft C++ Team Blog – Using C++17 Parallel Algorithms for Better Performance (2018)
  4. Teodorescu, Lucian – A Case Against Blind Use of C++ Parallel Algorithms, ACCU Overload 161 (2021)
  5. Stack Overflow – Difference between execution policies and when to use them (Philipp, 2017)
  6. Stack Overflow – C++ compiler support for std::execution (2021)