About conditional breakpoints

By Meeting C++ | Aug 18, 2022 07:11 AM | Tags: performance intermediate debugging debug basics advanced

A post on conditional breakpoints, including two surveys about their usage.

About conditional brealkpoits

by Jens Weller

From the article:

A few weeks ago someone asked me for advice on finding a specific bug in a larger C++ code base...

I don't remember much of the details, but one of the challenges was that at least some of the code based used public members, and in order to find the bug a change in these members is what they wanted to understand. Adding out put statements into a setter function wasn't possible, as the code did not have those. My suggestion was using a conditional breakpoint. And it also made me curious, if and how they're used with in our community.

Data Orientation For The Win! - Eduardo Madrid - CppCon 2021

By Adrien Hamelin | Jul 22, 2022 01:31 PM | Tags: performance

Registration is now open for CppCon 2022, which starts on September 11 and will again be held both in person and online. To whet your appetite for this year’s conference, we’re posting videos of some of the top-rated talks from last year’s conference. Here’s another CppCon talk video we hope you will enjoy – and why not register today for CppCon 2022 to attend in person, online, or both!

Data Orientation For The Win!

by Eduardo Madrid

Summary of the video:

C++ conferences have had presentations showing the important performance benefits of data-oriented design principles; however, the principles seem to require lots of "manual" effort and "code uglification"; these make the principles less practical, and there haven't been clear recommendations about how to deal with runtime-polymorphic types.

In this talk we will recapitulate on data orientation principles and their benefits showing their application through production-strength Generic Programming components made to support them.

Specific examples include:
1. Structures of arrays instead of arrays of complex structures (a.k.a. "scattering")
2. Support for data oriented designs for runtime-polymorphism without inheritance+virtual (the equivalent of using std::variant or std::function, but generalized as allowed by the Zoo type-erasure framework)
----1. Hybrid buffers: the equivalent of the virtual table pointer is scattered out of the objects solving the "Goldilocks problem" of how big the local buffer should be, objects occupy the available space optimally
----2. Easy (de)serialization through very easy relocatability
----3. Voiding the need for pointers in favor of indices into arrays

HPX V1.8.0 released -- STE||AR Group

By Hartmut Kaiser | May 25, 2022 07:59 AM | Tags: performance parallelism heterogeneous computing distributed computing concurrency c++23 c++20 c++17 c++14 c++11

The STE||AR Group has released V1.8.0 of HPX -- A C++ Standard library for Concurrency and Parallelism.

HPX V1.8.0 Released

We have released HPX 1.8.0 -- a major update to our C++ Standard Library for Concurrency and Parallelism. The HPX parallel algorithms now have been fully adapted to C++20, all existing facilities have been adjusted to conform to this version of the Standard as well. We now can proudly announce full conformance to the C++20 concurrency and parallelism facilities. HPX supports all of the algorithms as specified by C++20. On top of that we support parallel versions of all range-based algorithms and have added support for explicit vectorization to more of our algorithms (using std::experimental::simd). Much work has been done towards implementing P2300 (std::execution) and the underlying senders/receivers facilities. Last but not least, we have finished the refactoring of the whole library into a rather large set of non-cyclically depending components. Finally, the new release comes with a brand new documentation interface!

You can download the release from our releases page or check out the 1.8.0 tag using git. A full list of changes can be found in the release notes.

HPX is a general-purpose parallel C++ runtime system for applications of any scale. It implements all of the related facilities as defined by the C++20 Standard. As of this writing, HPX provides the only widely available open-source implementation of the new C++17 and C++20 parallel algorithms, including a full set of parallel range-based algorithms. Additionally, HPX implements functionalities proposed as part of the ongoing C++ standardization process, such as large parts of the features related parallelism and concurrency as specified by the upcoming C++23 Standard, the C++ Concurrency TS, Parallelism TS V2, data-parallel algorithms, executors, and many more. It also extends the existing C++ Standard APIs to the distributed case (e.g., compute clusters) and for heterogeneous systems (e.g., GPUs).

HPX seamlessly enables a new Asynchronous C++ Standard Programming Model that tends to improve the parallel efficiency of our applications and helps reducing complexities usually associated with parallelism and concurrency.

Modern C++ In-Depth — Move Semantics, Part 1

By Jens Maurer | Mar 4, 2022 03:27 PM | Tags: performance c++11 basics

Moves

Modern C++ In-Depth — Move Semantics, Part 1

by Ralph Kootker

From the article

This week, we’re beginning a new series of posts exploring some of the more technically challenging features of C++11 and beyond. [...] For our first topic, we’re going to take a look at how we can use move-semantics to avoid some potentially expensive copies.

C++20: Heterogeneous Lookup in (Un)ordered Containers--Bartlomiej Filipek

By Adrien Hamelin | Nov 28, 2021 11:45 PM | Tags: performance

Did you know?

C++20: Heterogeneous Lookup in (Un)ordered Containers

by Bartlomiej Filipek

From the article:

Would you like to gain 20…35 or even 50% speed improvements when searching in associative containers? In this blog post, we’ll explore a technique called “heterogenous access” that offers such impressive speedups. We’ll explore ordered containers, and the support for unordered collections added recently in C++20...

Three reasons to pass std::string_view by value--Arthur O’Dwyer

By Adrien Hamelin | Nov 12, 2021 01:14 PM | Tags: performance

You should.

Three reasons to pass std::string_view by value

by Arthur O’Dwyer

From the article:

It is idiomatic to pass std::string_view by value. Let’s see why.

First, a little background recap. In C++, everything defaults to pass-by-value; when you say Widget w you actually get a whole new Widget object. But copying big things can be expensive. So we introduce “pass-by-const-reference” as an optimization of “pass-by-value,” and we tell people to pass big and/or expensive things like std::string by const reference instead of by value.

But for small cheap things — int, char*, std::pair<int, int>, std::span<Widget> — we continue to prefer the sensible default behavior of pass-by-value.

Pass-by-value has at least three performance benefits over pass-by-(const)-reference. I’ll illustrate all three of them via string_view...

Beautiful C++: 30 Core Guidelines for Writing Clean, Safe, and Fast Code--Kate Gregory

By Adrien Hamelin | Nov 12, 2021 01:11 PM | Tags: performance

Are you applying them?

Beautiful C++: 30 Core Guidelines for Writing Clean, Safe, and Fast Code

by Kate Gregory

From the article:

It's true! I helped to write a book! Another book! (Do not ask me what this brings the total to, I truly do not know.) This one is about some of the C++ Core Guidelines...

C++ String Benchmark -- Giovanni Dicanio

By Giovanni Dicanio | Nov 4, 2021 11:55 AM | Tags: performance

This small article compares different string implementations on the Windows platform.

C++ String Benchmark: STL vs. ATL vs. Custom Pool Allocator

by Giovanni Dicanio

From the article:

I was curious to compare the performance of the STL string implementation versus ATL CString, using Visual Studio 2019, so I wrote some simple C++ benchmark code for this purpose. I also added into the mix a custom string pool allocator.

HPX V1.7.1 released -- STE||AR Group

By Hartmut Kaiser | Aug 13, 2021 08:49 AM | Tags: performance parallelism heterogeneous computing distributed computing concurrency c++20 c++17 c++14 c++11

The STE||AR Group has released V1.7.1 of HPX -- A C++ Standard library for parallelism and concurrency.

HPX V1.7.1 Released

The newest version of HPX (V1.7.1) is now available for download! This release fixes minor problems found after the version 1.7.0. It fixes a bug in the internals of actions, adds a version check to the new Asio dependency, and slightly improves the performance of spinlocks among other minor changes. Importantly, the experimental hpx::execution::simdpar execution policy introduced in 1.7.0 was renamed to hpx::execution::par_simd for consistency with the standard parallel execution policies. While this is a breaking change in a patch release, we felt it was important to make this adaptation as soon as possible. The full list of improvements, fixes, and breaking changes can be found in the release notes.

    HPX is a general purpose parallel C++ runtime system for applications of any scale. It implements all of the related facilities as defined by the C++ Standard. As of this writing, HPX provides one of the only widely available open-source implementation of the new C++17 parallel algorithms. Additionally, HPX implements functionalities proposed as part of the ongoing C++ standardization process, such as large parts of the features related parallelism and concurrency as specified by the upcoming C++20 Standard, the C++ Concurrency TS, Parallelism TS V2, data-parallel algorithms, executors, senders/receivers and many more. It also extends the existing C++ Standard APIs to the distributed case (e.g. compute clusters) and for heterogeneous systems (e.g. GPUs).

    HPX seamlessly enables a new Asynchronous C++ Standard Programming Model that tends to improve the parallel efficiency of our applications and helps reducing complexities usually associated with parallelism and concurrency.

How to Parallelise CSV Reader - C++17 in Practice--Bartlomiej Filipek

By Adrien Hamelin | Aug 11, 2021 11:44 AM | Tags: performance c++17

Would do it the same way?

How to Parallelise CSV Reader - C++17 in Practice

by Bartlomiej Filipek

Friom the article:

At C++Stories (and in my C++17 book) you can find several articles on Parallel Algorithms introduced in C++17. The examples included in those posts were usually relatively straightforward. How about writing something larger?

In this text, you’ll see how to build a tool that works on CSV files, parses lines into sales records and then performs calculations on the data.

You’ll see how easy it is to add parallel execution to selected algorithms and have a performance improvement across the whole application (for example 4.5x on 6 cores, including file loading). In the end, we’ll discuss problems that we found along the way and possible future enhancements.

Here’s the plan:

• How to build an application that loads CSV files
• How to efficiently use parallel algorithms
• How to use std::filesystem library to gather required files
• How to use other C++17 library features like std::optional, conversion routines - std::from_chars and string_view
• Where to add [[nodiscard]] and improve code readability

Let’s go...