Search Results for "benchmark windows"
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Showing 638 open source projects for "benchmark windows"
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Benchmark
A microbenchmark support library
A library to benchmark code snippets, similar to unit tests. -
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windows_hardening
HardeningKitty and Windows Hardening Settings
This repository, also known as HardeningKitty, is a comprehensive Windows hardening checklist for personal and enterprise environments. It translates security benchmarks (e.g., CIS, Microsoft Security Baselines) into actionable Group Policy and registry recommendations. Though designed primarily for Windows 10, it includes workaround modes such as “HailMary” for Windows Home users lacking the Group Policy Editor. -
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IPerf3-Windows
IPerf3 startup program for windows with GUI
This is an iperf3 for windows GUI startup program, no need to install. Just run it directly. Supports two startup services: iperf3 server and client. -
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XMRig
RandomX, KawPow, CryptoNight, AstroBWT and GhostRider unified miner
High performance, open-source, cross-platform RandomX, KawPow, CryptoNight, and AstroBWT CPU/GPU miner, RandomX benchmark, and stratum proxy. XMRig is a high-performance, open-source, cross-platform RandomX, KawPow, CryptoNight, and AstroBWT unified CPU/GPU miner and RandomX benchmark. Official binaries are available for Windows, Linux, macOS, and FreeBSD. The preferred way to configure the miner is the JSON config file as it is more flexible and human-friendly. ... -
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hyperfine
A command-line benchmarking tool
A command-line benchmarking tool. Statistical analysis across multiple runs. Support for arbitrary shell commands. Constant feedback about the benchmark progress and current estimates. Warmup runs can be executed before the actual benchmark. Cache-clearing commands can be set up before each timing run. Statistical outlier detection to detect interference from other programs and caching effects. Export results to various formats: CSV, JSON, Markdown, AsciiDoc. Parameterized benchmarks (e.g.... -
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Superposition Benchmark (Unigine)
GPU benchmark testing graphics performance with realistic 3D scenes.
Superposition Benchmark by Unigine is a powerful GPU stress-testing and benchmarking tool designed to evaluate graphics performance using the Unigine 2 Engine. It features advanced visuals, real-time lighting, and physics simulations to test DirectX and OpenGL performance. Superposition provides detailed results, including frame rates, GPU temperatures, and stability data. It supports VR mode and 4K to 8K resolutions. Widely used by gamers and hardware reviewers, it is proprietary but offers... -
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BEIR
A Heterogeneous Benchmark for Information Retrieval
BEIR is a benchmark framework for evaluating information retrieval models across various datasets and tasks, including document ranking and question answering. -
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kube-bench
Checks whether Kubernetes is deployed
kube-bench is a tool that checks whether Kubernetes is deployed securely by running the checks documented in the CIS Kubernetes Benchmark. Trivy, the all-in-one cloud-native security scanner, can be deployed as a Kubernetes Operator inside a cluster. Both, the Trivy CLI, and the Trivy Operator support CIS Kubernetes Benchmark scanning among several other features. There are multiple ways to run kube-bench. You can run kube-bench inside a pod, but it will need access to the host's PID... -
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GLM-4.6
Agentic, Reasoning, and Coding (ARC) foundation models
GLM-4.6 is the latest iteration of Zhipu AI’s foundation model, delivering significant advancements over GLM-4.5. It introduces an extended 200K token context window, enabling more sophisticated long-context reasoning and agentic workflows. The model achieves superior coding performance, excelling in benchmarks and practical coding assistants such as Claude Code, Cline, Roo Code, and Kilo Code. Its reasoning capabilities have been strengthened, including improved tool usage during inference... -
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BenchmarkTools.jl
A benchmarking framework for the Julia language
BenchmarkTools makes performance tracking of Julia code easy by supplying a framework for writing and running groups of benchmarks as well as comparing benchmark results. This package is used to write and run the benchmarks found in BaseBenchmarks.jl. The CI infrastructure for automated performance testing of the Julia language is not in this package but can be found in Nanosoldier.jl. Our story begins with two packages, "Benchmarks" and "BenchmarkTrackers". The Benchmarks package... -
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CrystalDiskMark
A simple disk benchmark software
A simple disk benchmark software. -
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AgentBench
A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)
AgentBench is an open-source benchmark designed to evaluate the capabilities of large language models when used as autonomous agents. Unlike traditional language model benchmarks that focus on static text tasks, AgentBench measures how models perform in interactive environments that require planning, reasoning, and decision-making. The benchmark includes multiple environments that simulate realistic scenarios such as web interaction, database querying, and problem solving tasks. These... -
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CrystalMark Retro
CrystalMark Retro is a comprehensive benchmarking software.
CrystalMark Retro is a comprehensive benchmarking software that runs on Windows 95 and Windows NT 3.51 or later. It supports 32-bit (x86), 64-bit (x64/ARM64), many-core, and multilingual (48+ languages) systems, and can measure CPU, Disk, 2D graphics (GDI), and 3D graphics (OpenGL) performance with a single click. Benchmark results can be registered in CrystalMarkDB for centralized management of past results (account required: free of charge) and comparison with data registered by users around the world. -
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SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the... -
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HumanEval
Code for the paper "Evaluating Large Language Models Trained on Code"
human-eval is a benchmark dataset and evaluation framework created by OpenAI for measuring the ability of language models to generate correct code. It consists of hand-written programming problems with unit tests, designed to assess functional correctness rather than superficial metrics like text similarity. Each task includes a natural language prompt and a function signature, requiring the model to generate an implementation that passes all provided tests. The benchmark has become a... -
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MTEB
MTEB: Massive Text Embedding Benchmark
Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding... -
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LongBench
LongBench v2 and LongBench (ACL 25'&24')
...LongBench addresses this gap by providing datasets that require models to process and reason over long sequences of text across multiple tasks. The benchmark includes multiple categories such as single-document question answering, multi-document reasoning, summarization, long dialogue understanding, and code analysis. It supports bilingual evaluation in English and Chinese to assess multilingual capabilities across extended contexts. Newer versions of the benchmark introduce extremely long context windows ranging from thousands to millions of tokens, enabling researchers to test the limits of modern long-context models. -
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Crow Framework
A Fast and Easy to use microframework for the web
A Fast and Easy to use microframework for the web. Crow is a C++ framework for creating HTTP or Websocket web services. It uses routing similar to Python's Flask which makes it easy to use. It is also extremely fast, beating multiple existing C++ frameworks as well as non-C++ frameworks. Crow is provided free of charge courtesy of everyone who is donating their money, time, and expertise to keep it going. The 1000-mile journey begins with a single step. Get started by installing Crow and... -
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MLPerf
Reference implementations of MLPerf™ training benchmarks
This is a repository of reference implementations for the MLPerf training benchmarks. These implementations are valid as starting points for benchmark implementations but are not fully optimized and are not intended to be used for "real" performance measurements of software frameworks or hardware. Benchmarking the performance of training ML models on a wide variety of use cases, software, and hardware drives AI performance across the tech industry. The MLPerf Training working group draws on... -
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AICGSecEval
A.S.E (AICGSecEval) is a repository-level AI-generated code security
AICGSecEval is an open-source benchmark framework designed to evaluate the security of code generated by artificial intelligence systems. The project was developed to address concerns that AI-assisted programming tools may produce insecure code containing vulnerabilities such as injection flaws or unsafe logic. The framework constructs evaluation tasks based on real-world software repositories and known vulnerability cases derived from CVE records. By simulating realistic development... -
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DeepSeek-OCR 2
Visual Causal Flow
DeepSeek-OCR-2 is the second-generation optical character recognition system developed to improve document understanding by introducing a “visual causal flow” mechanism, enabling the encoder to reorder visual tokens in a way that better reflects semantic structure rather than strict raster scan order. It is designed to handle complex layouts and noisy documents by giving the model causal reasoning capabilities that mimic human visual scanning behavior, enhancing OCR performance on documents... -
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SAM 3
Code for running inference and finetuning with SAM 3 model
SAM 3 (Segment Anything Model 3) is a unified foundation model for promptable segmentation in both images and videos, capable of detecting, segmenting, and tracking objects. It accepts both text prompts (open-vocabulary concepts like “red car” or “goalkeeper in white”) and visual prompts (points, boxes, masks) and returns high-quality masks, boxes, and scores for the requested concepts. Compared with SAM 2, SAM 3 introduces the ability to exhaustively segment all instances of an... -
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JMH Gradle Plugin
Integrates the JMH benchmarking framework with Gradle
The JMH Gradle Plugin provides integration of the Java Microbenchmark Harness (JMH) into Gradle builds, enabling developers to write and run performance benchmarks directly in their projects. JMH is the de facto standard for writing accurate and reliable Java microbenchmarks, and this plugin automates tasks like generating benchmark sources, compiling them with the required JMH support classes, and packaging runnable benchmark jars. It simplifies the workflow by handling classpath setup and... -
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Meta Agents Research Environments (ARE)
Meta Agents Research Environments is a comprehensive platform
Meta Agents Research Environments (ARE) is a simulation and benchmarking platform. It is designed to evaluate AI agents in dynamic, evolving, multi-step tasks. Unlike static benchmarks, ARE supports environments where agents must adapt to changes over time and reason over sequences of actions. It interacts with applications and faces uncertainty. The included Gaia2 benchmark offers 800 scenarios across multiple “universes”. It can test reasoning, memory, tool use, and adaptability.... -
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CodeGeeX
CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)
CodeGeeX is a large-scale multilingual code generation model with 13 billion parameters, trained on 850B tokens across more than 20 programming languages. Developed with MindSpore and later made PyTorch-compatible, it is capable of multilingual code generation, cross-lingual code translation, code completion, summarization, and explanation. It has been benchmarked on HumanEval-X, a multilingual program synthesis benchmark introduced alongside the model, and achieves state-of-the-art...
