This package is used for the detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR. It also provides the possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots.

Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification.

This is a package for inference of protein activity from gene expression data. It includes the VIPER and msVIPER algorithms

DoRothEA is a gene regulatory network containing signed transcription factor. DoRothEA regulons, the collection of a TF and its transcriptional targets, were curated and collected from different types of evidence for both human and mouse. A confidence level was assigned to each TF-target interaction based on the number of supporting evidence.

This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study.

This package provides more than 9900 annotated position frequency matrices from 14 public sources, for multiple organisms.

GROMACS is a versatile package to perform molecular dynamics, i.e. simulate the Newtonian equations of motion for systems with hundreds to millions of particles. It is primarily designed for biochemical molecules like proteins, lipids and nucleic acids that have a lot of complicated bonded interactions, but since GROMACS is extremely fast at calculating the nonbonded interactions (that usually dominate simulations) many groups are also using it for research on non-biological systems, e.g. polymers. GROMACS supports all the usual algorithms you expect from a modern molecular dynamics implementation.

The rfacts package is an R interface to the Fixed and Adaptive Clinical Trial Simulator FACTS. It programmatically invokes FACTS to run clinical trial simulations. It aggregates simulation output data into tidy data frames. These capabilities provide end-to-end automation for large-scale simulation pipelines, and they enhance computational reproducibility.

This package provides several cubic spline interpolation methods of H. Akima for irregular and regular gridded data are available through this package, both for the bivariate case and univariate case. Linear interpolation of irregular gridded data is also covered. A bilinear interpolator for regular grids was also added for comparison with the bicubic interpolator on regular grids.

This package provides the CUDA C++ developers with building blocks that make it easier to write safe and efficient code. It unifies three essential former CUDA C++ libraries into a single repository:

• Thrust (former repo)

• CUB (former repo)

• libcudacxx (former repo)

This package provides a GPU-accelerated library of primitives for deep neural networks, with highly tuned implementations for standard routines such as forward and backward convolution, attention, matmul, pooling, and normalization.

OpenCL (Open Computing Language) is a multi-vendor open standard for general-purpose parallel programming of heterogeneous systems that include CPUs, GPUs and other processors. This package provides the API to use OpenCL on NVIDIA GPUs.

This package provides a system-wide performance analysis tool designed to visualize an application’s algorithms, identify the largest opportunities to optimize, and tune to scale efficiently across any quantity or size of CPUs and GPUs,from large servers to small systems-on-a-chip.

This package provides a minimal low-level profiling API for CUDA.

This package provides a set of APIs which can be used at runtime to link together GPU devide code. It supports Link Time Optimization.

This package provides a an interactive profiler for CUDA and NVIDIA OptiX that provides detailed performance metrics and API debugging via a user interface and command-line tool. Users can run guided analysis and compare results with a customizable and data-driven user interface, as well as post-process and analyze results in their own workflows.

This binary extracts information from CUDA binary files (both standalone and those embedded in host binaries) and presents them in human readable format. The output of cuobjdump includes CUDA assembly code for each kernel, CUDA ELF section headers, string tables, relocators and other CUDA specific sections. It also extracts embedded ptx text from host binaries.

This package provides a high-performance, GPU accelerated JPEG decoding functionality for image formats commonly used in deep learning and hyperscale multimedia applications. The library offers single and batched JPEG decoding capabilities which efficiently utilize the available GPU resources for optimum performance; and the flexibility for users to manage the memory allocation needed for decoding.

The nvJPEG library enables the following functions: use the JPEG image data stream as input; retrieve the width and height of the image from the data stream, and use this retrieved information to manage the GPU memory allocation and the decoding. A dedicated API is provided for retrieving the image information from the raw JPEG image data stream.

The encoding functions of the nvJPEG library perform GPU-accelerated compression of user’s image data to the JPEG bitstream. User can provide input data in a number of formats and colorspaces, and control the encoding process with parameters. Encoding functionality will allocate temporary buffers using user-provided memory allocator.

This package decodes (demangles) low-level identifiers that have been mangled by CUDA C++ into user readable names. For every input alphanumeric word, the output of cu++filt is either the demangled name if the name decodes to a CUDA C++ name, or the original name itself.

This package provides a command-line tool to profile CUDA kernels. It enables the collection of a timeline of CUDA-related activities on both CPU and GPU, including kernel execution, memory transfers, memory set and CUDA API calls and events or metrics for CUDA kernels.

This package provides the NVIDIA tool for debugging CUDA applications running. CUDA-GDB is an extension to GDB, the GNU Project debugger. The tool provides developers with a mechanism for debugging CUDA applications running on actual hardware. This enables developers to debug applications without the potential variations introduced by simulation and emulation environments.

This package provides a set of APIs which can be used at runtime to combine multiple CUDA objects into one CUDA fat binary (fatbin). The APIs accept inputs in multiple formats, either device cubins, PTX, or LTO-IR. The output is a fatbin that can be loaded by cuModuleLoadData of the CUDA Driver API. The functionality in this library is similar to the fatbinary offline tool in the CUDA toolkit, with the following advantages:

• Support for runtime fatbin creation.

• The clients get fine grain control over the input process.

• Supports direct input from memory, rather than requiring inputs be written to files.

This package provides the CUDA run-time support libraries for NVIDIA GPUs, all of which are proprietary.

This package provides a set of GPU-accelerated basic linear algebra subroutines used for handling sparse matrices that perform significantly faster than CPU-only alternatives. Depending on the specific operation, the library targets matrices with sparsity ratios in the range between 70%-99.9%.