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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.