profiling

Two official PyTorch tutorials linking to the 'Profiling in PyTorch' series are highlighted, providing guidance on using the PyTorch profiler API for performance debugging.

CodeSizer is a static code size profiling tool for embedded firmware that uses objdump and addr2line to attribute code size to inline call trees and generates an HTML report.

A beginner-friendly guide to using torch.profiler in PyTorch for profiling and optimizing deep learning workloads, covering trace reading, CUDA analysis, and torch.compile integration.

This tutorial demonstrates how to profile various attention implementations in PyTorch, from naive attention to scaled dot-product attention with different backends, using the PyTorch profiler on an NVIDIA A100 GPU.

A deep dive into Go's profiling mechanism, explaining how the runtime collects CPU, heap, block, mutex, and goroutine profiles, and how they are represented in the pprof format.

The article explores how agent tools are being used to encode development workflows for SGLang, turning debugging, benchmarking, and profiling into executable skills and reproducible experiments, with efforts like KDA-Pilot already producing merged PRs.

The paper introduces VirtueMap, a framework that profiles large language models by evaluating their rankings of ethical dilemma responses through an Aristotelian virtue ethics lens, using a validated common-sense ground truth.

A half-day tutorial at ISC High Performance 2026 on using compiler-assisted tools (FPChecker/LLVM) for floating-point error analysis and profiling in C/C++ scientific codes.

KernelPro is a closed-loop multi-agent system that uses LLMs and micro-profiling tools to automatically optimize GPU kernel code, achieving geomean speedups of 2.42×/4.69×/5.30× on KernelBench and demonstrating a measured 11.6% energy reduction at matched speed.

Introduces SocialPersona, a benchmark for evaluating multimodal large language models on their ability to recover revealed preferences from longitudinal social-media timelines and use them in personalized dialogue.

Analysis showing that GPUs used for AI training often sit idle waiting for data, questioning the severity of the GPU shortage.

The author shares excitement about kernel fusion and demonstrates using HuggingFace's kernels project to profile a GeGLU FFN fused Liger kernel, noting the profile's beauty.

Announces Part 2 of a profiling tutorial covering linear layer tracing, gemm epilogues, MLP tracing, and comparisons of torch compile vs Liger kernels, with a link to the full content.

This blog post continues the profiling in PyTorch series, exploring nn.Linear, MLP blocks, and fusion techniques using Triton kernels to optimize performance.

Google's AI strategy is criticized as a surveillance-based profiling engine that forces users into consent through mandatory login, circumventing GDPR. The article exposes Google's plan to replace traditional search with AI-generated answers and personalized tracking, calling it a legal loophole wrapped in AI hype.

This paper finds that human psychometric questionnaires fail to reliably predict LLM behavior in real-world interactions, and proposes generation-based profiling as a more accurate alternative.

A beginner-friendly guide to using PyTorch's torch.profiler for profiling and optimizing neural network operations, starting with matrix multiplication and bias addition. It explains how to read profiler traces and understand CPU/GPU interactions.

Sayak Paul describes a project to profile and optimize Diffusers pipelines using torch.compile, and announces a tutorial series by Ari G. on the topic.

Sensor Tower's engineering team used graph theory analysis and profiling tools to identify bottlenecks in a slow backend endpoint, achieving a fourfold speed improvement by optimizing Protobuf decoding and encoding steps.

This tweet introduces three financial/engineering tools open-sourced by top quantitative institutions such as Jane Street, Goldman Sachs, and J.P. Morgan: magic-trace (high-precision process tracing), gs-quant (Python package for derivatives pricing and risk management), and Perspective (real-time data visualization tool), helping quant enthusiasts gain institutional-level technical capabilities for free.