Projects sharing features with Go Torch

vprof is a visual profiling tool for Python designed to identify execution bottlenecks and monitor memory consumption. It functions as a CPU and memory profiler that transforms performance data into interactive visualizations to analyze processor time and call stacks. The project distinguishes itself through a suite of visual diagnostics, including flame graphs for stack visualization and heatmaps that map execution frequency and duration directly onto source code. It also includes a remote performance monitor capable of capturing function-specific metrics from a running server and streaming

Async-profiler is a suite of performance tools designed for sampling Java runtimes, tracking memory allocations, and monitoring hardware counters. It functions as a low-overhead sampling profiler for Java applications, collecting stack traces and memory allocation data without safepoint bias. The project provides specialized utilities for generating interactive flame graphs to visualize execution hotspots in a web browser. It includes a hardware performance counter monitor to track low-level system events such as cache misses and page faults. The toolset covers several diagnostic domains, in

Bytehound is a Linux memory profiler that utilizes a custom global allocator to intercept memory requests and track allocations and deallocations. It records full call-stack traces for every memory operation to map allocations back to their originating source code. The project features a remote memory profiling system that streams capture data via network sockets to a separate machine, minimizing resource overhead on the target system. Analysis is supported by a specialized domain-specific query language used to automate the detection of memory patterns and anomalies. The tool covers heap al

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

Async-profiler is a sampling profiler for Java applications that tracks CPU time and stack traces across execution frames to identify performance bottlenecks. It is designed to capture profiling data without introducing timing bias. The project provides capabilities for JVM memory analysis to locate native and heap allocation hotspots and memory leaks. It also includes system contention analysis to identify resource bottlenecks through the tracking of contended locks and hardware performance counters. The tool converts raw profiling data into visual performance reports, including interactive

Bistoury is a production diagnostics tool for Java applications that provides a distributed debugging console, a performance profiler, and a runtime bytecode manipulator. It enables real-time application debugging and production diagnostics by analyzing running Java applications through dynamic instrumentation and state inspection. The system distinguishes itself through a remote agent manager that coordinates diagnostic connections and a runtime bytecode manipulator capable of redefining classes in memory without requiring process restarts. It features a web interface for capturing heap dump

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

Hotspot is a graphical user interface for analyzing and visualizing performance data captured by the Linux perf tool. It functions as a performance profiling visualizer and assembly-level profiler that maps performance costs to specific instructions synchronized with original source code. The project distinguishes itself through a remote symbol resolver that maps performance data from embedded targets to local host debug symbols and sysroots. It also includes a specialized off-CPU analysis tool designed to identify thread wait times and I/O blocks using kernel scheduler tracepoints. The tool

FlameGraph is a performance profiling and visualization toolkit designed to identify bottlenecks in software execution. It functions as a processing engine that transforms raw stack trace samples into interactive, hierarchical diagrams. By representing aggregated execution frequency as nested rectangles, the tool allows developers to visualize hot code paths and analyze system behavior across both kernel and user-space environments. The project distinguishes itself through its ability to perform differential profile analysis, which highlights performance regressions or improvements by compari

Parca is an always-on continuous profiling platform that captures CPU and memory usage from running applications without any code modifications. It uses eBPF kernel-level tracing to automatically discover and sample stack traces across infrastructure, and provides a web-based flame graph dashboard for interactive performance analysis. Its label-based query engine lets users slice and aggregate profiling data across dimensions such as service, container, or region, using a Prometheus-style selector syntax. Unlike basic profilers, Parca stores profile samples in a columnar format using Apache A

Tracy is a real-time performance profiling framework for C and C++ applications. It provides a software instrumentation library that captures high-resolution telemetry data, which is then visualized through a separate graphical interface to identify bottlenecks and resource allocation issues. The system utilizes a client-server architecture that enables remote profiling, allowing performance data to be captured on a target machine and analyzed on a workstation. It employs lock-free event logging and shared-memory ring buffers to minimize the overhead of data collection, ensuring that the main

Orbit is a set of specialized tools for C and C++ performance profiling, binary symbol mapping, and remote process and thread analysis. It provides a system for analyzing execution time and resource usage, utilizing a call graph visualizer to map function entries and exits into hierarchical execution flows for individual threads. The project distinguishes itself with a remote process profiler capable of capturing performance data from applications running on remote hosts. It also includes a thread scheduling analyzer that tracks context switches and processor core utilization to visualize thr

btrace is a JVM dynamic tracing tool and performance profiler used for injecting safe instrumentation scripts into a running Java Virtual Machine without requiring a process restart. It functions as a Java agent framework and a Model Context Protocol server, exposing JVM diagnostic operations and tracing tools to large language models and AI assistants. The project distinguishes itself by enabling real-time code injection and bytecode-level instrumentation via a secure binary protocol. It ensures production stability through a static safety analysis engine that blocks unstable code patterns,

The 1BRC (One Billion Row Challenge) is a Java performance benchmarking exercise that processes one billion temperature records from a text file to compute the minimum, mean, and maximum temperature per weather station. At its core, it is a large-scale data aggregation challenge designed to test how efficiently a Java program can parse and aggregate structured data from a plain text file, serving as both a programming exercise and a benchmark for Java performance optimization. The project distinguishes itself through a collection of performance-oriented architectural patterns for high-through

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

This project is a Java runtime diagnostic tool and bytecode instrumentation framework. It provides a remote troubleshooting interface for inspecting live Java systems, analyzing execution traces, and monitoring method performance without requiring application restarts. The system distinguishes itself through its ability to modify Java classes at runtime to capture parameters and return values, combined with a JavaScript-based scripting engine for custom diagnostic logic. It further supports collaborative live debugging, allowing multiple users to connect to a single remote process simultaneou

Arthas is a Java diagnostic tool and runtime debugger designed for real-time troubleshooting of applications. It functions as a remote diagnostics agent that allows users to inspect the runtime state of a Java process, including its heap objects and classloader hierarchies, without requiring a process restart. The project distinguishes itself through advanced bytecode manipulation capabilities, enabling live class hotswapping and the modification of bytecode in running processes. It supports in-memory source compilation and runtime bytecode decompilation to verify and update logic instantly w

Perfetto is a platform for system-level performance tracing and analysis on Linux and Android. It combines a high-throughput trace recorder, a SQL-based query engine, and a browser-based visualizer into a single toolchain. The platform covers CPU scheduling and call-stack profiling, native and Java heap memory allocation tracking, GPU and graphics events, and system-wide counters such as CPU frequency and power consumption. The architecture decouples trace recording from offline analysis, using a compact protobuf format for event encoding and columnar storage for efficient SQL queries. The we

Node-clinic is a performance profiling suite for Node.js designed to diagnose CPU bottlenecks, memory leaks, and asynchronous event loop issues. It provides specialized diagnostic tools for visualizing asynchronous flows, generating CPU flame graphs, and analyzing heap memory allocation. The suite includes a CPU flame graph tool to identify functions consuming the most processing time, a heap memory analyzer to track object allocation and leaks, and an asynchronous I/O profiler to map operation flows and find event loop bottlenecks. These tools cover broader capabilities in CPU execution pro

BCC is an eBPF development toolkit and tracing framework used for monitoring and analyzing the Linux kernel. It functions as a performance analysis tool and debugging utility to capture system events, measure kernel latency, and provide network observability. The project distinguishes itself by providing a build system that integrates with LLVM to compile C-like code into BPF bytecode at runtime. It utilizes BPF Type Format data for relocations to maintain cross-kernel compatibility and extracts kernel headers to ensure the generated programs match the specific kernel version. The toolkit co

pprof is a tool for visualizing and analyzing performance profiling data. It converts sampled call stacks into a directed graph rendered as an SVG, enabling visual identification of execution hotspots. The tool also parses Linux perf.data files, converting them into an internal profile representation for further analysis. Beyond visualization, pprof provides a command-line REPL for interactive exploration of profiling data, allowing users to filter, refine, and query performance information on the fly. It generates sorted text reports that highlight the most resource-intensive call stacks, an

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

backward-cpp is a C++ stack trace library and debugging utility designed to capture, resolve, and print detailed execution traces and crash reports. It functions as a crash reporter and call stack printer that translates raw memory addresses into human-readable function names, filenames, and line numbers. The project provides automated crash reporting by registering system handlers for fatal errors, such as segmentation faults, to automatically generate execution traces upon program failure. It distinguishes itself by extracting source file fragments from disk to display specific lines of cod

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

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 performance analysis tool for Ruby applications using the Rack interface. It monitors request execution times and resource usage, serving as a profiler for web applications to measure latency and identify bottlenecks. The tool provides specific analyzers for database query performance, memory allocations, and garbage collection statistics. It generates call stack flamegraphs to visualize time distribution across methods and renders speed badges and timing metrics directly onto HTML pages. The system covers broader performance profiling capabilities including custom code blo

Pyroscope is a continuous profiling platform designed to collect, store, and visualize application performance data. It functions as an application performance management suite that tracks historical resource usage to identify bottlenecks and detect performance regressions over time. The platform distinguishes itself through its use of kernel-level instrumentation and dynamic runtime hooks, which allow for performance monitoring without requiring manual code modifications or application restarts. It employs a sidecar agent architecture to offload telemetry processing, utilizing delta-encoded