performance

How to optimize C and C++ code in 2018—Iurii Krasnoshchok

Are you aware?

How to optimize C and C++ code in 2018

by Iurii Krasnoshchok

From the article:

We are still limited by our current hardware. There are numerous areas where it just not good enough: neural networks and virtual reality to name a few. There are plenty of devices where battery life is crucial, and we must count every single CPU tick. Even when we’re talking about clouds and microservices and lambdas, there are enormous data centers that consume vast amounts of electricity.

Even boring tests routine may quietly start to take 5 hours to run. And this is tricky. Program performance doesn‘t matter, only until it does.

A modern way to squeeze performance out of silicon is to make hardware more and more sophisticated...

How to Boost Performance with Intel Parallel STL and C++17 Parallel Algorithms—Bartlomiej Filipek

Another one.

How to Boost Performance with Intel Parallel STL and C++17 Parallel Algorithms

by Bartlomiej Filipek

From the article:

C++17 brings us parallel algorithms. However, there are not many implementations where you can use the new features. The situation is getting better and better, as we have the MSVC implementation and now Intel’s version will soon be available as the base for libstdc++ for GCC.

Since the library is important, I’ve decided to see how to use it and what it offers...

A zero cost abstraction?—Josh Peterson

Safe and performant?

A zero cost abstraction?

by Josh Peterson

From the article:

Recently Joachim (CTO at Unity) has been talking about “performance by default”, the mantra that software should be as fast as possible from the outset. This is driving the pretty cool stuff many at Unity are doing around things like ECS, the C# job system, and Burst (find lots more about that here).

One question Joachim has asked internally of Unity developers is (I’m paraphrasing here): “What is the absolute lower bound of time this code could use?” This strikes me as a really useful way to think about performance. The question changes from “How fast is this?” to “How fast could this be?”. If the answers to those two questions are not the same, the next question is “Do we really need the additional overhead?”

Another way to think about this is to consider the zero-cost abstraction, a concept much discussed in the C++ and Rust communities. Programmers are always building abstractions, and those abstractions often lead to the difference between “how fast it is” and “how fast it could be”. We want to provide useful abstractions that don’t hurt performance...

The Amazing Performance of C++17 Parallel Algorithms, is it Possible?—Bartlomiej Filipek

Are you using it?

The Amazing Performance of C++17 Parallel Algorithms, is it Possible?

by Bartlomiej Filipek

From the article:

With the addition of Parallel Algorithms in C++17, you can now easily update your “computing” code to benefit from parallel execution. In the article, I’d like to examine one STL algorithm which naturally exposes the idea of independent computing. If your machine has 10-core CPU, can you always expect to get 10x speed up? Maybe more? Maybe less? Let’s play with this topic.

HPX V1.2 released—STE||AR Group

The STE||AR Group has released V1.2 of HPX -- A C++ Standard library for parallelism and concurrency.

HPX V1.2 Released

The newest version of HPX (V1.2) is now available for download! Please see here for the release notes. This release is the first in our more frequent release schedule. We are aiming to produce one release every six months in an effort to get new features and stable releases out to users more quickly.

    HPX exposes an API fully conforming to the parts of the C++11/C++14/C++17 standards that are related to parallelism and concurrency, extended and applied to distributed and heterogeneous computing, and aligned with the ongoing standardization discussions.

    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 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 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 which tends to improve the parallel efficiency of our applications and helps reducing complexities usually associated with parellism and concurrency.

 

Using C++17 Parallel Algorithms for Better Performance—Billy O’Neal

Are you using the parallel capacities of the std?

Using C++17 Parallel Algorithms for Better Performance

by Billy O’Neal

From the article:

C++17 added support for parallel algorithms to the standard library, to help programs take advantage of parallel execution for improved performance. MSVC first added experimental support for some algorithms in 15.5, and the experimental tag was removed in 15.7.

The interface described in the standard for the parallel algorithms doesn’t say exactly how a given workload is to be parallelized. In particular, the interface is intended to express parallelism in a general form that works for heterogeneous machines, allowing SIMD parallelism like that exposed by SSE, AVX, or NEON, vector “lanes” like that exposed in GPU programming models, and traditional threaded parallelism.

Our parallel algorithms implementation currently relies entirely on library support, not on special support from the compiler. This means our implementation will work with any tool currently consuming our standard library, not just MSVC’s compiler. In particular, we test that it works with Clang/LLVM and the version of EDG that powers Intellisense...

First Meeting Embedded Conference Schedule available

Meeting Embedded is a new conference with a focus on embedded, hosting lots of talks connected to embedded & C++, plus a keynote by Dan Saks!

Meeting Embedded 2018

Schedule

Organized by Jens Weller

From the article:

Meeting Embedded 2018 is a one day event focused on hard and software development for embedded and the IoT. Meeting Embedded will be at Vienna House Andel's Berlin Hotel on the 14th of November, right in front of Meeting C++!