One of our customers has a mathematical simulation program where precision matters. We were given the code for evaluation and asked to make it run faster. They have a strict requirement that the source code is compiled without any optimization flags that can influence precision. Most compilers offer a fast-math compiler flag that significantly improves […]
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Is your algorithm running at peak performance? The roofline model
There are worlds where performance matters. For example HPC world: faster software means less wait time for the scientists. Embedded world: faster software means we can use cheaper silicon to build our product. Game world: faster software means that our game will run on slower CPUs, thus making our game more interesting to people with […]
Case Study: How we made the Canny edge detector run faster? (part 2)
In the previous post we talked about the performance improvement we obtained for the Canny edge detection algorithm. Most of the changes we did there were focused on hot loops: making memory accesses sequential and making the loops longer, which in turn allowed the compiler to autovectorize them. This made our program run significantly faster […]
A touch of parallelism: example of NPB CG Benchmark
The ultimate goal of the Codee software suite is to help users achieve the peak performance of their software. One of the ways to do it is with a touch of parallelism. This post will talk about the NPB CG benchmark, a popular benchmark for comparing supercomputers, developed by NASA. We will talk about how […]
Case Study: How we made Canny edge detector algorithm run faster? (part 1)
Canny edge detector algorithm is a famous algorithm in image processing. One of our customers was inquiring about the performance of that algorithm, so our performance engineers took a look. As always when dealing with unfamiliar codebases, we didn’t know what to expect. Some algorithms have a lot of room for performance improvements, others don’t. […]
CESGAHACK5 returns to accelerate the execution time of applications of scientists and developers
The Galicia Supercomputing Center (CESGA) and Codee (formerly Appentra Solutions) will host the 5th CESGAHACK virtually, from 14th to 18th December 2020. The five-day virtual hackathon aims to help scientists and application developers to answer questions by speeding up their application runtime, and reducing how long they spend coding the improved runtime. This year the […]
Codee winner of the ‘Innovative Science’ category at the Innovation Radar Prize 2020
The European Commission has awarded prizes to Europe’s most promising innovations emerging from EU funded research and innovation projects. The 12 finalists competed in three prize categories: “Women-led innovation”, “Tech for Society” and “Innovative Science”. The final of the Innovation Radar Prize 2020 took place online on September 24, 2020 during the European Commission’s R&I […]
Codee (formerly Appentra) raises €1.8M co-led by Armilar Venture Partners and K Fund for its software
Today we are excited to announce that Codee (Appentra Solutions) has raised €1.8M in a round led by Armilar Venture Partners and K Fund, also joined by Caixa Capital Risc, Xesgalicia and Unirisco, the investors that had already trusted us in the very early stages of the company. During the last months we have been […]
Enabling fast user selection in 5G networks through a GPU enabled maxvol algorithm
Executive Summary A team from the King Abdullah University of Science and Technology (KAUST) attended the GPU training organized by the KAUST Supercomputing Lab (KSL), NVIDIA and Appentra to learn how to improve the performance of their “maxvol” software for 5G wireless networks using GPUs. In 5G networks the base station can send data to […]
Bringing European parallel programming technology to the Exascale era
Barcelona, 20 November 2018. Launched on 1 October 2018, the European EPEEC project (European joint Effort toward a Highly Productive Programming Environment for Heterogeneous Exascale Computing) has received €3.9 M in funding to develop and deploy a production-ready parallel programming environment. This will turn future exascale supercomputers, which will be overwhelmingly heterogeneous, into manageable platforms […]
