QCDNUM numerically solves the DGLAP evolution equations on a discrete grid in x and Q2. You can evolve unpolarised parton density functions in NNLO, and polarised PDFs or fragmentation functions in NLO.

YODA is a small set of data analysis (specifically histogramming) classes being developed by MCnet members as a lightweight common system for MC event generator validation analyses, particularly as the core histogramming system in Rivet.

CLHEP is a set of HEP-specific foundation and utility classes such as random generators, physics vectors, geometry and linear algebra. CLHEP is structured in a set of packages independent of any external package.

This package provides a set of helpers for matplotlib to more easily produce plots typically needed in HEP as well as style them in way that's compatible with current collaboration requirements (ROOT-like plots for CMS, ATLAS, LHCb, ALICE).

This package provides a library for statistical inference aiming to cover the needs High Energy Physics.

This package provides a Python implementation of a statistical model for multi-bin histogram-based analysis and its interval estimation is based on the asymptotic formulas of "Asymptotic formulae for likelihood-based tests of new physics". The aim is also to support modern computational graph libraries such as PyTorch and TensorFlow in order to make use of features such as autodifferentiation and GPU acceleration.

APFEL is a library able to perform DGLAP evolution up to NNLO in QCD and to NLO in QED, both with pole and MSbar masses. The coupled DGLAP QCD+QED evolution equations are solved in x-space by means of higher order interpolations and Runge-Kutta techniques.

ADIOS2 transports data as groups of self-describing variables and attributes across different media types (such as files, wide-area-networks, and remote direct memory access) using a common application programming interface for all transport modes. ADIOS2 can be used on supercomputers, cloud systems, and personal computers.

IOR is a parallel IO benchmark that can be used to test the performance of parallel storage systems using various interfaces and access patterns. The IOR repository also includes the mdtest benchmark which specifically tests the peak metadata rates of storage systems under different directory structures. Both benchmarks use a common parallel I/O abstraction backend and rely on MPI for synchronization.

StarPU is a run-time system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible.

PaRSEC is a generic framework for architecture aware scheduling and management of micro-tasks on distributed many-core heterogeneous architectures. Applications are expressed as a Direct Acyclic Graph of tasks with labeled edges designating data dependencies. PaRSEC assigns computation threads to the cores, overlaps communications and computations between nodes as well as between host and accelerators (like GPUs). It achieves these features by using a dynamic, fully-distributed scheduler based on architectural features such as NUMA nodes and GPU awareness, as well as algorithmic features such as data reuse.

StarPU is a run-time system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible.

StarPU is a run-time system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible.

StarPU is a run-time system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible.

ViTE is a trace explorer. It is a tool to visualize execution traces of parallel programs (OpenMP, MPI, etc.) in Pajé or OTF format for debugging and profiling parallel or distributed applications. Such traces can be obtained using, for example, EZTrace.

Get v8 stack traces as an array of CallSite objects.

Check if a protocol requires a certain port number to be added to an URL.

Tiny millisecond conversion utility

Tools for debugging your node.js modules and event loop

The Node.js `util.deprecate()` function with browser support

Parser and generator for CSS color strings

strftime for JavaScript

A list of color names and its values

Date formatting and parsing