Search Results for "system benchmark"
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Showing 530 open source projects for "system benchmark"
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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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LZ4
Extremely fast compression algorithm
LZ4 is lossless compression algorithm, providing compression speed > 500 MB/s per core (>0.15 Bytes/cycle). It features an extremely fast decoder, with speed in multiple GB/s per core (~1 Byte/cycle). A high compression derivative, called LZ4_HC, is available, trading customizable CPU time for compression ratio. LZ4 library is provided as open-source software using a BSD license. This benchmark simulates simple "static content transfer" scenario such as OS Kernel compression or video game's... -
3
KonaBess
A GPU overclock & undervolt tool for various Snapdragon chips
KonaBess is a straightforward application designed to customize GPU frequency and voltage tables without the need for kernel recompilation. The application achieves customization by unpacking the Boot/Vendor Boot image, decompiling and editing relevant dtb (device tree binary) files, and finally repacking and flashing the modified image. The extent of improvement varies, with some users reporting a 25% reduction in power consumption in the graphics benchmark (4.2w->3.2w) after undervolting... -
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HunyuanWorld-Voyager
RGBD video generation model conditioned on camera input
HunyuanWorld-Voyager is a next-generation video diffusion framework developed by Tencent-Hunyuan for generating world-consistent 3D scene videos from a single input image. By leveraging user-defined camera paths, it enables immersive scene exploration and supports controllable video synthesis with high realism. The system jointly produces aligned RGB and depth video sequences, making it directly applicable to 3D reconstruction tasks. At its core, Voyager integrates a world-consistent video... -
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5
K9s
Kubernetes CLI To Manage Your Clusters In Style!
... support to extend K9s to create your very own cluster commands. Powerful filtering mode to allow user to drill down and view workload related resources. Supports for viewing RBAC rules such as cluster/roles and their associated bindings. Reverse lookup to asserts what a user/group or ServiceAccount can do on your clusters. You can benchmark your HTTP services/pods directly from K9s to see how your application fare and adjust your resources request/limit accordingly. -
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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. vary... -
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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 implemented... -
8
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... -
9
youki
A container runtime written in Rust
youki is an implementation of the OCI runtime-spec in Rust, similar to runc. Rust is one of the best languages to implement the oci-runtime spec. Many very nice container tools are currently written in Go. However, the container runtime requires the use of system calls, which requires a bit of special handling when implemented in Go. This is too tricky (e.g. namespaces(7), fork(2)); with Rust, it's not that tricky. And, unlike in C, Rust provides the benefit of memory safety. While Rust... -
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10
TorchServe
Serve, optimize and scale PyTorch models in production
TorchServe is a performant, flexible and easy-to-use tool for serving PyTorch eager mode and torschripted models. Multi-model management with the optimized worker to model allocation. REST and gRPC support for batched inference. Export your model for optimized inference. Torchscript out of the box, ORT, IPEX, TensorRT, FasterTransformer. Performance Guide: built-in support to optimize, benchmark and profile PyTorch and TorchServe performance. Expressive handlers: An expressive handler... -
11
Snappy
A fast compressor/decompressor
... 250 MB/sec or more and decompresses at about 500 MB/sec or more. (These numbers are for the slowest inputs in our benchmark suite; others are much faster.) In our tests, Snappy usually is faster than algorithms in the same class (e.g. LZO, LZF, QuickLZ, etc.) while achieving comparable compression ratios. -
12
Nightingale
A distributed and high-performance monitoring system
Nightingale is a distributed and high-performance monitoring system. Prometheus enterprise edition. Nightingale is a new generation of domestic intelligent monitoring system. It has good support for cloud-native scenarios and traditional physical machine virtual machine scenarios. It takes 10 minutes to complete the construction and is familiar with use in 1 hour. It has been verified by the massive data of the production environment of Didi, and hopes to create a benchmark for domestic... -
13
Likwid
Performance monitoring and benchmarking suite
Likwid is a simple to install and use toolsuite of command line applications and a library for performance oriented programmers. It works for Intel, AMD, ARMv8 and POWER9 processors on the Linux operating system. There is additional support for Nvidia and AMD GPUs. There is support for ARMv7 and POWER8/9 but there is currently no test machine in our hands to test them properly. -
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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. Integration... -
15
CapFrameX
Frametime capture and analysis tool
CapFrameX is a performance capture and analysis tool designed for PC gamers and benchmarking enthusiasts, which records frametimes, frame-rates and various sensor data during game or application execution. It uses backend tools like PresentMon to log data and then exposes a comprehensive UI for analyzing the results: you can view charts of frametimes, historic graphing, stuttering analysis, L-shape graphs, input-lag overlays, and compare multiple capture runs side by side. Importantly, the... -
16
PyG
Graph Neural Network Library for PyTorch
... graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour). -
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PlatEMO
Evolutionary multi-objective optimization platform
Evolutionary multi-objective optimization platform. PlatEMO consists of a number of MATLAB functions without using any other libraries. Any machines able to run MATLAB can use PlatEMO regardless of the operating system. PlatEMO includes more than ninety existing popular MOEAs, including genetic algorithm, differential evolution, particle swarm optimization, memetic algorithm, estimation of distribution algorithm, and surrogate model-based algorithm. Most of them are representative algorithms... -
18
Docker Bench For Security
A script that checks for dozens of common best-practices around Docker
... with a lot of privilege, sharing the host's filesystem, pid and network namespaces, due to portions of the benchmark applying to the running host. The easiest way to run your hosts against the Docker Bench for Security is by running our pre-built container. Don't forget to adjust the shared volumes according to your operating system. -
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Apache Sedona
Cluster computing framework for processing large-scale geospatial data
Apache Sedona™ is a cluster computing system for processing large-scale spatial data. Sedona extends existing cluster computing systems, such as Apache Spark and Apache Flink, with a set of out-of-the-box distributed Spatial Datasets and Spatial SQL that efficiently load, process, and analyze large-scale spatial data across machines. According to our benchmark and third-party research papers, Sedona runs 2X - 10X faster than other Spark-based geospatial data systems on computation-intensive... -
20
plow
A high-performance HTTP benchmarking tool
Plow is an HTTP(S) benchmarking tool, written in Golang. It uses excellent fast HTTP instead of Go's default net/http due to its lightning-fast performance. Plow runs at specified connections (option -c) concurrently and real-time records summary statistics, histogram of execution time and calculates percentiles to display on Web UI and terminal. It can run for a set duration( option -d), for a fixed number of requests(option -n), or until Ctrl-C is interrupted. The implementation of... -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot... -
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TextDistance
Compute distance between sequences
Python library for comparing the distance between two or more sequences by many algorithms. For main algorithms, text distance try to call known external libraries (fastest first) if available (installed in your system) and possible (this implementation can compare this type of sequences). Install text distance with extras for this feature. Textdistance use benchmark results for algorithm optimization and try to call the fastest external lib first (if possible). TextDistance show benchmarks... -
23
MemU
MemU is an open-source memory framework for AI companions
MemU is an agentic memory layer for LLM applications, specifically designed for AI companions. Transform your memory into an intelligent file system that automatically organizes, connects, and evolves with your memories. Simple, fast, and reliable memory infrastructure for AI applications. Powerful tools and dedicated support to scale your AI applications with confidence. Full proprietary features, commercial usage rights, and white-labeling options for your enterprise needs. SSO/RBAC... -
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Rbatis
Rust High Performance compile-time ORM(RBSON based)
A highly Performant, Safe, Dynamic SQL(Compile-time) ORM framework written in Rust, inspired by Mybatis and MybatisPlus. Zero cost Dynamic SQL, implemented using (proc-macro,compile-time, Cow (Reduce unnecessary cloning)) techniques, don't need ONGL engine(mybatis) Free deserialization, Auto Deserialize to any struct(Option,Map,Vec...) High performance, Based on Future, with async_std/tokio, single threaded benchmark can easily achieve 200,000 QPS. logical deletes, pagination, py-like SQL... -
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RecBole
A unified, comprehensive and efficient recommendation library
... and developing recommendation algorithms in a unified, comprehensive and efficient framework for research purpose. It can be installed from pip, conda and source, and is easy to use. We have implemented more than 100 recommender system models, covering four common recommender system categories in RecBole and eight toolkits of RecBole2.0, including General Recommendation, Sequential Recommendation, Context-aware Recommendation, and Knowledge-based Recommendation and sub-packages.
