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2025

High-Performance Computing: The Fortran Optimization Manifesto

This post details the engineering journey to optimize the Binary Trees benchmark, aiming to surpass the performance of the fastest C++ implementation. Through rigorous profiling, micro-architectural analysis, and the application of modern Fortran techniques, we achieved a >50% reduction in execution time (0.76s vs 1.60s) on an Intel Ivy Bridge architecture. This case study demonstrates that language choice is secondary to algorithmic understanding, memory architecture mastery, and compiler-assisted optimization.

  • in HPC

High-Performance Computing: The Fortran Optimization Manifesto

This post details the engineering journey to optimize the Binary Trees benchmark, aiming to surpass the performance of the fastest C++ implementation. Through rigorous profiling, micro-architectural analysis, and the application of modern Fortran techniques, we achieved a 20% reduction in execution time (1.27s vs 1.60s) on an Intel Ivy Bridge architecture. This case study demonstrates that language choice is secondary to algorithmic understanding, memory architecture mastery, and compiler-assisted optimization.

  • in Linux

The Guide to Stabilizing Linux on a MacBook Pro (Mid-2012)

Installing Linux (specifically Kubuntu 22.04) on a MacBook Pro 9,2 (Mid-2012 / A1278) breathes new life into this classic hardware. However, out of the box, users often face a frustrating array of issues: the system freezing upon waking from sleep, random reboots when plugging in power, and crashes after leaving the laptop closed overnight.

Optimizing Scientific Workloads: Architectural Constraints and Algorithmic Strategies

The efficiency of scientific computing is governed by the synergy between hardware architecture and software implementation. This analysis explores the technical transition from consumer-grade legacy processors (Ivy Bridge and Sandy Bridge microarchitectures) to enterprise-grade server architectures (Broadwell), detailing the implications for instruction sets, virtualization, and cache memory management. Furthermore, it examines strategies for optimizing Fortran code to exploit these architectural distinctives through manual loop tiling and compiler-driven transformations using the Graphite framework.

  • in PINN

Unpacking an Advanced Parametric PINN: A Deep Dive into Smart Training Techniques

Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for solving differential equations, but how can we make them more robust, efficient, and capable of solving entire families of problems? The answer lies in advanced training strategies that guide the network toward a better solution.

This post will deconstruct the key components of an advanced parametric PINN, using a Python script that solves the 2D Burgers' equation as our practical example. We'll explore how techniques like curriculum learning, adaptive weighting, and intelligent learning rate scheduling come together to create a powerful and generalizable model.

  • in PINN

Why Is My AI Failing at Physics? A Guide to Debugging Physics-Informed Neural Networks

Physics-Informed Neural Networks, or PINNs, represent a fascinating intersection of artificial intelligence and the natural sciences. Imagine you want to model a complex physical system, like the flow of water in a river or the formation of a traffic jam. You might have a few real-world measurements, but not nearly enough to train a traditional AI model. This is where PINNs shine. They are neural networks trained not just on data, but also on the mathematical equations that govern the system (Raissi et al., 2019). By baking the laws of physics directly into the training process, PINNs can often find accurate solutions even with very sparse data.

  • in PINN

Bridging the Software Gap: Accelerating Physics-Informed Neural Networks

In the realm where artificial intelligence meets scientific discovery, Physics-Informed Neural Networks (PINNs) stand out as a revolutionary approach. These networks don't just learn from data; they are inherently guided by the fundamental laws of physics, allowing them to solve complex equations and uncover hidden parameters in physical systems (Raissi et al., 2019). Imagine a digital brain that understands both observations and the underlying rules of nature. This capability is particularly valuable when experimental data is scarce or noisy, as the physics acts as a powerful constraint on the learning process.

  • in ANN

Achieving Scalable Performance on Commodity Hardware for Large Model Training

Training large-scale artificial intelligence models has become a defining challenge of the modern computational era, often perceived as a domain exclusive to those with access to state-of-the-art, unencumbered supercomputing infrastructure. However, recent advancements have demonstrated that exceptional performance can be achieved even with resource constraints and heterogeneous or restricted hardware. This is accomplished through a sophisticated synthesis of software optimization, algorithmic innovation, and a deep understanding of the underlying system architecture. This post delves into the technical strategies that enable high-throughput training, focusing on innovations in parallelism, communication, and low-precision arithmetic.

  • in PINN

Enhancing Parametric PINN Training: A Synergistic Approach with Weighted Curricula and Learning Rate Scheduling

Physics-Informed Neural Networks (PINNs) have emerged as a powerful paradigm for solving differential equations by embedding physical laws directly into the loss function of a neural network. A particularly compelling application is in solving parametric inverse problems, where the goal is to infer physical parameters (e.g., viscosity, conductivity) from observational data. A common and effective methodology involves a two-stage process: first, training a general parametric model over a range of the parameter, and second, using this pre-trained model as a prior to rapidly identify the specific parameter value that best explains a new set of observations.

  • in PINN

Overcoming Generalization Challenges in 2D Burgers' Equation with Physics-Informed Neural Networks

Physics-Informed Neural Networks (PINNs) have emerged as a powerful paradigm in "scientific machine learning," enabling the integration of physical laws, expressed as Partial Differential Equations (PDEs), into neural network training (Wang et al., 2021, p. 1; Lu et al., 2021, p. 208). While PINNs offer a promising avenue for solving complex, non-linear systems like the 2D Burgers' equation, ensuring robust generalization remains a critical challenge, particularly for solutions exhibiting multi-scale features, anisotropic behavior, or in data-poor environments. The 2D Burgers' equation is a fundamental non-linear PDE often used as a benchmark for such challenges (Raissi et al., 2019, p. 686). This post explores several cutting-edge approaches aimed at enhancing PINN generalization capabilities, directly addressing the underlying limitations that hinder their performance.