clpeak is a cross-platform synthetic micro-benchmark that measures peak achievable compute performance (FLOPS/TOPS), bandwidth, latency, and AI inference throughput across CPUs, GPUs, and NPUs. It supports eight hardware backends: OpenCL, Vulkan, CUDA, ROCm/HIP, Metal, oneAPI/SYCL, native CPU (with per-ISA dispatch), and ONNX Runtime. The tool is architected as a set of backend-specific static libraries orchestrated by a common framework, with two front-ends: a CLI tool (`clpeak`) and a Flutter desktop/mobile GUI. The GUI communicates with the native library via a C-ABI FFI bridge, streaming structured events for live display. Results are serialized to a versioned JSON schema (v3) and can be compared against baselines. The codebase is notable for its meticulous anti-optimization engineering (MAD chains, compiler barriers, calibration probes) and its protobuf-free ONNX model generator. It is a mature, actively maintained project with extensive CI coverage and packaging for multiple platforms.
Vegeta is a versatile HTTP load testing tool and Go library. It simulates constant-rate or oscillating traffic against HTTP endpoints, collects latency and byte metrics, and generates reports (text, JSON, histograms, HTML plots). The core is an attack engine with pluggable pacers, a targeter for reading HTTP request specs, and a metrics pipeline using t-digest for percentile estimates. It includes a Prometheus exporter and a downsampling library for plotting large time series. The codebase is well-structured with clean package boundaries, cross-platform support, and extensive testing including fuzzing.
SmokePing is a mature network latency monitoring and visualization application written in Perl. It measures round-trip time and packet loss to targets using a wide variety of pluggable probes (ICMP, TCP, HTTP, DNS, SSH, SNMP, etc.), stores the time-series data in RRD files, and presents interactive web-based graphs via a CGI/FastCGI interface. The architecture includes a daemon that runs probes on a schedule, a master/slave mode for distributed monitoring, and an alerting system that can trigger notifications based on configurable patterns or matcher plugins. While primarily an application, it also includes data analysis components such as matchers and sorters that compute statistics (median, loss, standard deviation) from stored data. The codebase is monolithic in parts (Smokeping.pm >3000 lines) and uses outdated JavaScript libraries, but offers well-designed extensibility through probe base classes and a self-contained SNMP stack (BER.pm, SNMP_Session.pm) that could be reused independently.