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libxir1

Xilinx Intermediate Representation (XIR) for deep learning algorithms (runtime)

Xilinx Intermediate Representation (XIR) is a graph based intermediate representation of the AI algorithms which is well designed for compilation and efficient deployment of the Domain-specific Processing Unit (DPU) on the FPGA platform. Advanced users can apply Whole Application Acceleration to benefit from the power of FPGA by extending the XIR to support customized IP in Vitis AI flow.

libtrilinos-aztecoo-13.2

object-oriented interface to the Aztec solver - runtime files

AztecOO provides an object-oriented interface to the well-known Aztec solver library. Furthermore, it allows flexible construction of matrix and vector arguments via Epetra matrix and vector classes. Finally, AztecOO provide additional functionality not found in Aztec and any future enhancements to the Aztec package will be available only through the AztecOO interfaces.

libtrilinos-anasazi-13.2

large-scale eigenvalue algorithms - runtime files

Anasazi is an extensible and interoperable framework for large-scale eigenvalue algorithms. The motivation for this framework is to provide a generic interface to a collection of algorithms for solving large-scale eigenvalue problems. Anasazi is interoperable because both the matrix and vectors (defining the eigenspace) are considered to be opaque objects -- only knowledge of the matrix and vectors via elementary operations is necessary.

libfilezilla21

build high-performing platform-independent programs (runtime lib)

Free, open source C++ library, offering some basic functionality to build high-performing, platform-independent programs. Some of the highlights include:

libjxmpp-java

base library for XMPP based instant messaging and presence

JXMPP is an Open Source Java base library for XMPP. It provides often used functionality needed to build an XMPP stack.

libstdgpu-openmp0d

Efficient STL-like Data Structures on the GPU (OpenMP CPU backend)

stdgpu is an open-source library providing several generic GPU data structures for fast and reliable data management. Multiple platforms such as CUDA, OpenMP, and HIP are supported allowing you to rapidly write highly complex agnostic and native algorithms that look like sequential CPU code but are executed in parallel on the GPU.