Large language models have sparked a lot of attention in the research community in recent days, especially with the introduction of practical tools such as ChatGPT and Github Copilot. Their ability to solve complex programming tasks was also shown in several studies and commercial solutions increasing the interest in using them for software development in different fields. High performance computing is one of such fields, where parallel programming techniques have been extensively used to utilize raw computing power available in contemporary multicore and manycore processors. In this paper, we perform an evaluation of the ChatGPT and Github Copilot tools for OpenMP-based code parallelization using a proposed methodology. We used nine different benchmark applications which represent typical parallel programming workloads and compared their OpenMP-based parallel solutions produced manually and using ChatGPT and Github Copilot in terms of obtained speedup, applied optimizations, and quality of the solution. ChatGPT 3.5 and Github Copilot installed with Visual Studio Code 1.88 were used. We concluded that both tools can produce correct parallel code in most cases. However, performance-wise, ChatGPT can match manually produced and optimized parallel code only in simpler cases, as it lacks a deeper understanding of the code and the context. The results are much better with Github Copilot, where much less effort is needed to obtain correct and performant parallel solution.

This research paper explores the efficacy of WebAssembly technology in enhancing the performance of web applications, with a focus on graphic-intensive domains such as video games, simulations, and image processing. WebAssembly, a binary instruction format designed for browsers, offers performance comparable to desktop applications by executing code within a secure, low-level virtual machine environment. The study further examines the integration of WebAssembly into various programming languages such as C/C++, C#, and Rust, highlighting its cross-platform capabilities and efficient memory management. Through the development of a website that hosts retro-themed video games, the paper assesses performance across different platforms, demonstrating that while unrestricted desktop platforms show higher speeds, regulated environments exhibit negligible performance discrepancies. The findings suggest that while WebAssembly substantially boosts web application performance, opportunities for further enhancements in areas such as garbage collection, debugging, and JavaScript integration remain. This research underscores WebAssembly's potential to revolutionize web application development, providing a robust framework for future advancements in complex web-based applications.

Large numbers of small tensor kernels are executed by GPUs in modern deep learning frameworks, where total performance is frequently constrained by memory bandwidth and kernel launch overheads. Systems such as TensorFlow XLA, PyTorch JIT, and cuDNN often use kernel fusion, which is defined as combining many tensor operations into a single GPU kernel, to reduce intermediate memory transfers and boost efficiency. Nevertheless, it is difficult to measure the true performance impact of fusion on both isolated tensor operations and end-to-end model execution. An experimental investigation of kernel fusion on three different NVIDIA GPUs is presented in this work. For four sample tensor operations: element-wise addition, fused multiply–add, linear transformation with ReLU activation, and map-reduce, we build fused and unfused CUDA kernels using FP32, FP16, and mixed-precision arithmetics. We measure execution time, speedup, and effective memory bandwidth across a range of input sizes. For memory-bound and activation-heavy workloads, fusion yields consistent speedups between 1.5× and 3.13×, particularly for small and medium inputs where kernel launch overhead is significant. For operations dominated by atomic updates, the benefit is limited to between 1.01× and 1.44×. When the reduction strategy is reformulated using block-level shared-memory aggregation, kernel fusion becomes effective again, achieving speedups of up to 2× by eliminating global synchronization bottlenecks. We further evaluate the effect of fusion on image classification models using PyTorch 2.10.0 JIT, achieving 1.54× to 1.83× faster inference. Our results provide practical guidelines on when kernel fusion is most effective.