Projects sharing features with Memory Leak Detector

Memray is a memory profiler for Python that tracks heap allocations in both Python code and native C or C++ extensions. It captures memory events by hooking into the language runtime and traversing call stacks, providing a comprehensive view of how an application consumes memory. The tool is designed to minimize performance impact on the target application by using thread-local buffering and streaming data to an external process or file. The project distinguishes itself through its ability to monitor complex, multi-threaded systems and child processes in real-time. It provides diagnostic util

gperftools is a collection of specialized tools for profiling CPU usage, detecting memory errors, and providing high-performance memory allocation. It provides a memory profiling toolkit for C++ applications, including a sampling CPU profiler and a heap profiler for analyzing consumption patterns. The project includes a high-performance memory allocator designed as a multi-threaded replacement for standard allocation to reduce contention and improve execution speed. It further provides a memory debugger to identify illegal memory access and double frees. The toolkit covers broad diagnostic c

Memlab is an automated browser memory profiler and JavaScript memory leak analyzer. It provides a toolkit for detecting and analyzing memory leaks by inspecting and comparing heap snapshots to identify unbound object growth and detached DOM elements. The system distinguishes itself through an automated leak testing framework that executes end-to-end browser interaction sequences to programmatically isolate memory regressions. It utilizes heap snapshot diffing, retainer chain tracing, and heuristic-based filtering to determine why objects remain in memory and to map the shortest path from garb

Memory Profiler is a diagnostic library for Ruby applications designed to monitor runtime memory consumption and object lifecycles. It provides tools to track object allocations and memory usage, enabling the identification of performance bottlenecks and potential memory leaks that affect software stability. The tool functions by observing memory behavior during program execution, allowing developers to distinguish between short-lived data and objects that persist beyond their intended lifecycle. It captures the execution context of allocations by walking the call stack, which helps attribute

This project is an educational resource providing a comprehensive development tutorial for writing and loading eBPF programs using C, Go, and Rust within the Linux kernel. It serves as a technical guide for developing custom logic to execute directly in the kernel. The materials cover specialized domains including kernel observability and tracing, security implementation for intrusion detection, and high-performance network engineering for packet filtering and load balancing. It also includes dedicated manuals for Linux kernel tracing and the use of kprobes, uprobes, and tracepoints. The pro

This project is a comprehensive technical guide and diagnostic manual for analyzing memory, performance, and asynchronous behavior within Node.js applications. It provides detailed methods for asynchronous tracing, memory diagnostics, and performance analysis to resolve runtime errors and execution bottlenecks. The resource distinguishes itself by covering advanced diagnostic workflows, including the use of flame graphs for CPU profiling, the capture and comparison of heap snapshots for memory leak detection, and the mapping of asynchronous call stacks. It also provides technical guidance on

OSV is a unikernel operating system and cloud-native execution environment designed to run as a secure microVM on hypervisors such as KVM, Firecracker, Xen, and VMware. It functions as a Linux binary compatible runtime, allowing unmodified Linux binaries to be executed as secure microVMs without requiring recompilation. The project distinguishes itself through its ability to package applications into minimal bootable images and its provide of a virtual machine management API. This REST interface enables remote monitoring of system health, management of execution traces, and control over guest

MLeaksFinder is a diagnostic framework and profiling utility designed for the automatic detection of memory leaks and unfreed objects in iOS applications. It functions as a memory leak detector and profiling tool that identifies retain cycles and analyzes object lifecycles for both Swift and Objective-C code. The tool identifies circular dependencies by tracing reference chains and traversing object graphs, starting from a root view controller. It provides precise debugging data by capturing the allocation stacks of leaked objects to trace the origin of the memory leak. The framework include

stats.js is a JavaScript performance monitor and visual diagnostic tool. It provides a real-time overlay for tracking frame rates, memory allocation, and the rendering efficiency of web graphics and applications. The project includes a visual meter for measuring frames per second and a browser memory profiler that displays allocated memory in megabytes to help detect resource leaks. It is designed as a web graphics debugger to monitor the efficiency of WebGL and Canvas rendering. The tool covers a range of monitoring and observability capabilities, including the creation of custom performanc

This project is a suite of analytical tools for quantifying web performance, specifically designed for benchmarking the rendering speed and memory usage of various JavaScript frameworks. It provides a standardized set of DOM manipulation tests and a comparison tool that uses weighted geometric means to measure efficiency across different web implementations. The benchmark harness distinguishes itself by providing deep analysis of DOM reconciliation strategies, comparing the performance and correctness of keyed versus non-keyed rendering. It also includes a memory profiler for tracking allocat

BenchmarkDotNet is a library and tool suite for measuring the execution time and memory allocation of .NET code. It utilizes statistical sampling and warm-up iterations to determine the stability and precise execution speed of specific methods. The project provides a JIT disassembly viewer to inspect processor disassembly and analyze how the compiler executes code paths. It includes a memory allocation profiler that tracks managed and native memory traffic to identify efficiency bottlenecks. Additionally, a runtime performance comparator allows the same benchmarks to be executed across differ

LeakCanary is a diagnostic tool designed to identify memory leaks by monitoring object lifecycles and analyzing heap snapshots. It automatically detects objects that fail to be garbage collected after their expected lifespan, providing developers with actionable insights to prevent performance degradation and application crashes. The project distinguishes itself by offloading memory-intensive heap parsing to a separate background process, which minimizes performance impact on the main application during runtime. It includes sophisticated deobfuscation capabilities that map obfuscated stack tr

Matrix is a suite of mobile application performance management and analysis tools. It provides a plugin-based monitoring system for capturing crashes, lags, and memory leaks, alongside a static binary auditor for reducing installation package size and a bytecode instrumentation tool for performance tracking. The project distinguishes itself through native memory debugging and a SQLite query linter that identifies inefficient database patterns. It employs native interception techniques to detect memory leaks and heap corruption without requiring source code recompilation, and uses a custom run

GodEye is a runtime instrumentation tool and observability framework for Swift mobile applications. It functions as a mobile application debugger and in-process diagnostic overlay, providing a visual interface rendered directly over active applications to monitor metrics and inspect system states without requiring changes to the original source code. The framework enables real-time analysis through a suite of debugging workflows. It captures diagnostic data using method swizzling and system-level hooking to intercept function calls and monitor internal application behavior. The tool covers s

This project is a suite of runtime diagnostic tools designed to detect memory leaks, concurrency races, and language-specification violations during software execution. It provides a collection of dynamic analysis tools that identify addressability issues, uninitialized memory usage, and memory safety bugs in applications. The toolset includes a thread safety analyzer to identify data races and deadlocks in concurrent code, as well as an undefined behavior sanitizer to detect operations that violate language specifications. The system covers broad capabilities in memory safety monitoring and

GCViewer is a JVM garbage collection visualizer and memory analysis tool. It functions as a log parser and metrics exporter that transforms verbose JVM garbage collection logs into structured data, visual charts, and summary reports. The project enables the visualization of heap size, generation usage, and collection timing through multi-line charts. It specifically tracks stop-the-world pause durations, concurrent collection cycles, and memory footprint to assist in detecting memory leaks and tuning heap sizes. The tool covers log processing capabilities such as timestamp alignment and the

PerfView is a set of integrated profiling and tracing tools built on Windows Event Tracing for Windows (ETW), designed to diagnose CPU, memory, and ETW-based performance issues in .NET applications. It captures ETW events to analyze runtime behavior, CPU usage, and memory allocations, serving as a .NET performance profiler that measures both CPU time and garbage collection events. The tool distinguishes itself with a diff comparison engine that compares two performance traces side-by-side to highlight changes in method costs and event rates. It renders profiled call stacks as flame graphs whe

This project provides a collection of reference materials, guides, and cheatsheets designed to optimize the Android development workflow. It serves as a comprehensive resource for implementing best practices in application building, debugging, and user interface design. The repository covers specialized techniques for build optimization, including methods to reduce binary sizes and accelerate compilation. It also provides detailed references for device debugging, memory leak detection, and the application of Material Design principles. The project further details productivity enhancements fo

DoKit is a frontend development debugging toolset designed for web and mobile applications. It provides a suite of utilities for intercepting network traffic, mocking API responses, inspecting UI hierarchies, and monitoring mobile app performance. The project is distinguished by its focus on hybrid app inspection, allowing developers to execute scripts within web views and browse internal application sandboxes. It includes a visual UI audit tool with alignment rulers and color pickers to verify that interfaces match design specifications, as well as a diagnostic system that tracks CPU usage a

LearningNotes is a technical knowledge base and engineering study guide focused on Android framework internals, system architecture, and mobile performance optimization. It serves as a reference for analyzing the Android boot sequence, process bootstrapping, and system service initialization. The project provides detailed guides on mobile performance, including strategies for reducing memory footprints, identifying memory leaks, and optimizing image decoding. It further covers Android inter-process communication using AIDL and the Binder kernel driver, as well as software architecture manuals

This project is a garbage collection library and memory allocator for C and C++ that provides automatic reclamation of unreachable objects. It functions as a memory management system that can replace standard allocation functions to automate memory reclamation without requiring source modification. The system is distinguished by its ability to perform incremental and generational garbage collection to reduce application pauses, as well as parallel collection to distribute tracing across multiple CPU cores. It includes a specialized string manipulation library that uses shared structures to en

The PyTorch Tutorials repository is a collection of educational resources that provides step-by-step guidance on building, training, and deploying neural networks using the PyTorch framework. It covers the complete machine learning workflow, from data loading and model definition through optimization loops and model persistence, with dedicated guides for distributed training, model fine-tuning, and deployment. The tutorials offer practical demonstrations of adapting pre-trained models to new tasks through transfer learning, scaling training across multiple GPUs or machines using PyTorch's dis

Segment Anything Fast is a high-performance computer vision inference engine and image segmentation framework built for PyTorch. It provides a specialized environment for automated object isolation and mask generation, designed to process large-scale visual datasets with increased throughput. The project distinguishes itself through a suite of system-level optimization strategies that accelerate deep learning model performance. By utilizing graph-based model compilation, just-in-time kernel fusion, and hardware-aware quantization, it reduces computational latency and memory footprint. These t

Scalene is a high-performance diagnostic utility designed to measure resource consumption during the execution of Python applications. It functions as a line-level monitor, providing granular insights that pinpoint the specific source code responsible for performance overhead. The tool distinguishes itself through statistical profiling that captures stack traces and resource usage without requiring manual instrumentation of the source code. It tracks CPU, GPU, and memory consumption by intercepting library-level calls and hardware driver commands, allowing for the analysis of both managed and

This project is a curated collection of Android development code snippets, implementation patterns, and technical guides designed to assist in building and maintaining mobile applications. It serves as a reference for standard mobile architecture, providing structured approaches to common development requirements and system integration tasks. The repository distinguishes itself by offering specific technical strategies for managing application lifecycles, optimizing memory usage, and ensuring interface responsiveness in resource-constrained environments. It provides programmatic techniques fo

This project is a comprehensive performance programming guide and reference for the Go language, focusing on runtime efficiency and memory optimization. It provides a collection of patterns and techniques designed to increase execution speed by reducing garbage collection overhead and optimizing memory usage. The resource distinguishes itself through detailed reference implementations for memory optimization, such as escape analysis, object pooling, and structure memory alignment. It offers specific strategies for reducing binary size and improving CPU cache efficiency through structure memor

This project is a browser developer tool designed for inspecting JavaScript execution, network traffic, and page layouts. It functions as a JavaScript debugger and a Chrome DevTools Protocol debugger to manage the state of a web engine and identify logic errors in web applications. The suite provides specialized utilities for web performance profiling, including the detection of memory leaks and the analysis of processing bottlenecks. It also includes a network traffic analyzer for troubleshooting API calls and a browser storage manager for modifying cookies, cache, and local database entries