Automatic translation of programming languages has garnered renewed interest, driven by recent advancements in large language models (LLMs). Encoder-decoder transformer models, in particular, have shown promise in translating between different programming languages. However, translating between a language and its high-performance computing (HPC) extension remains underexplored due to inherent challenges like complex parallel semantics understanding. In this paper, we introduce CodeRosetta, an encoder-decoder transformer model explicitly designed for translating between programming languages and also their HPC extensions. CodeRosetta is evaluated on C to CUDA and Fortran to C translation.It employs a customized learning-based framework with tailored pretraining and training objectives that enable it to effectively capture code semantics and parallel structural nuances, allowing for bidirectional code translation. Our results show that CodeRosetta outperforms state-of-the-art baselines in C to CUDA translation by 2.9 BLEU and 1.72 CodeBLUE points while improving compilation accuracy by 6.05%.

In this series, we are going to write a neural net completely from scratch (down to rudimentary tensor operations) with Nvidia's CUDA, the GPU parallel computing platform behind modern deep learning libraries. You can find the GitHub repository here. Audience: Familiarity with core concepts of C, such as pointers and object-oriented programming, is needed, and you must be thoroughly comfortable with neural networks and their various aspects like backpropagation. However, you need not know any CUDA or parallel programming, and everything will be covered in the upcoming articles. Without further ado, let's get coding!

Due to sufficient support suitable for exploration and introduction to parallel programming, actuator interface, Linux-based programming, deep learning, and artificial intelligence application development, the Jetson Nano developer kit is definitely suitable for a maker to get started with exciting advanced projects in robotics, computer vision, and IoT. The Nvidia community also provides a platform to share various Jetson projects from the community. Nvidia provides the JetPack SDK which is a collection of libraries, software, and OS options optimized for the Jetson boards. The Linux4Tegra OS is a GNU Linux special distribution by Nvidia for their Tegra series processors used in the Jetson boards. CUDA for GPU acceleration, TensorRT, cuDNN, and OpenCV are few of the widely used packages pre-installed on the OS image.

The inputs were 96x96 images, and the target outputs were 30-value vectors indicating x,y pairs for 15 facial keypoints. We had to design a CNN from scratch to perform the task. My architecture was three convolutional layers, each followed by a max pooling layer with dropout, then a two-layer dense regression network at the end. Training was done on an EC2 p2xlarge GPU instance, and took around 10 minutes to perform 250 epochs (though there was a lot of trial and error so all told I spent a few hours on training different architectures). The dataset came from this Kaggle competition!

While 2016 was the year of the GPU for a number of reasons, the truth of the matter is that outside of some core disciplines (deep learning, virtual reality, autonomous vehicles) the reasons why you would use GPUs for general purpose computing applications remain somewhat unclear. As a company whose products are tuned for this exceptional compute platform, we have a tendency to assume familiarity, often incorrectly. Our New Year's resolution is to explain, in language designed for business leaders, what a GPU is and why you should care. Let's get started by baselining on existing technology - the CPU. Most of us are familiar with a CPU.

How to determine whether your tasks can be parallelized on multi-core, high-memory machines General purpose techniques in R and Python for parallel programming Specific applications of parallel programming in a machine learning context, including how to speed up cross-validation, grid search, and random forest calculations Finally, how to use Domino for easy access to powerful multi-core machines where you can utilize these techniques. Finally, how to use Domino for easy access to powerful multi-core machines where you can utilize these techniques.

The secret to big data is of course the ability to do work in parallel. Modern Big Data engines like Hadoop don't rely on the invention of clever new algorithms or artificial intelligence to produce impressive results; instead, they are based on the idea of taking lots of inputs, working on little pieces of it in lots of places at the same time, then bringing the results together. Usually, the results are much smaller than the inputs, small enough that human beings can look at them directly. In order to work on lots of small pieces at the same time, whatever task we're performing has to be structured to be run in parallel. Many algorithms we're used to seeing work well sequentially have to be tweaked at least a little to work in parallel, and some have to be discarded as unusable in parallel.