"FastR is an implementation of the R Language in Java atop Truffle, a framework for building self-optimizing AST interpreters.
FastR is a GraalVM
based implementation of the R language that provides significant
improvements in the performance of R code, the embedding of the R
execution engine into Java applications, and the ability to interface
with other GraalVM and JVM languages including Python, Java, and Scala.
FastR is also able to utilize the tooling support provided by the GraalVM ecosystem.
https://github.com/oracle/fastr
https://medium.com/graalvm/faster-r-with-fastr-4b8db0e0dceb
Where the chairs are arranged with exquisite precision, and the rosin bag is always full. Or perhaps (yet) another attempt to keep track of those things of which we think we need to keep track.
Friday, October 12, 2018
WeasyPrint
"WeasyPrint is a visual rendering engine for HTML and CSS that can export
to PDF. It aims to support web standards for printing.
WeasyPrint is free software made available under a BSD license.
It is based on various libraries but not on a full rendering engine like WebKit or Gecko. The CSS layout engine is written in Python, designed for pagination, and meant to be easy to hack on."
https://github.com/Kozea/WeasyPrint
It is based on various libraries but not on a full rendering engine like WebKit or Gecko. The CSS layout engine is written in Python, designed for pagination, and meant to be easy to hack on."
https://github.com/Kozea/WeasyPrint
percollate
"Percollate is a command-line tool to turn web pages as beautifully formatted PDFs."
https://github.com/danburzo/percollate
https://github.com/danburzo/percollate
libgdf
"libGDF is a C library for implementing common functionality for a GPU
Data Frame. The goal is to agree on a standard way for libraries and
applications to exchange tabular data directly on the GPU, avoiding
architectural bottleneck of having to move data off the GPU just to pass
it to another library.
https://github.com/rapidsai/libgdf
https://github.com/rapidsai/libgdf
GPU Data Frame
"The basic approach for the GPU Data Frame (GDF) is pretty simple: if
applications and libraries agree on an in-memory data format for tabular
data and associate metadata, then just a device pointer to the data
structure need be exchanged. Additionally, the IPC mechanism built into
the CUDA driver allows device pointers to be moved between processes.
The GDF uses the Apache Arrow columnar format to represent data on the GPU. Some Arrow features are not yet supported."
http://gpuopenanalytics.com/#/
https://www.omnisci.com/blog/end-to-end-on-the-gpu-with-the-gpu-data-frame-gdf
https://www.anaconda.com/blog/developer-blog/bringing-dataframe-acceleration-gpu-rapids-project/
https://docs.anaconda.com/anaconda/user-guide/tasks/gpu-packages/
The GDF uses the Apache Arrow columnar format to represent data on the GPU. Some Arrow features are not yet supported."
http://gpuopenanalytics.com/#/
https://www.omnisci.com/blog/end-to-end-on-the-gpu-with-the-gpu-data-frame-gdf
https://www.anaconda.com/blog/developer-blog/bringing-dataframe-acceleration-gpu-rapids-project/
https://docs.anaconda.com/anaconda/user-guide/tasks/gpu-packages/
pygdf
"PyGDF implements the Python interface to access and manipulate the GPU DataFrame of GPU Open Analytics Initiative (GoAi). We aim to provide a simple interface that is similar to the Pandas DataFrame and hide the details of GPU programming."
Read more about GoAi and the GDF
https://github.com/rapidsai/pygdf
Read more about GoAi and the GDF
https://github.com/rapidsai/pygdf
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