Beyond Functional Programming: Manipulate Functions with the J Language

"In this article we'll get a brief introduction to the J programming language. The goal is to wet your appetite for an exciting programming paradigm rather than providing an in-depth introduction. As we move along we'll learn just enough J to explore a fascinating corner of the language that lets us manipulate functions as data. You'll see how these techniques differ from traditional functional programming, why manipulating functions is interesting and how it lets you solve real problems with a minimum of code."

http://www.adamtornhill.com/articles/jlang/beyondfunctional.html

Tagsistant

"Tagsistant is a semantic file system for Linux, a personal tool to catalog files using tags (labels, mnemonic informations) rather than directories.

Tagsistant replace the concept of directory with that of tag, but since it have to do with directories, it pairs the concept of tag with that of directory. So within Tagsistant a tag is a directory and a directory is a tag.

To be more precise, this is not true with all the directories. First level directories are special. The one called tags/ hosts all the tags. This means that every directory created inside it is infact a tag.

Another, called store/, hosts contents, like files. All the tags created inside tags/ are available inside store/. To tag a file all you have to do is to copy it inside one or more directories under store/.

Another special first level directory is relations/. Inside it you can establish relations between tags using mkdir."

https://github.com/StrumentiResistenti/Tagsistant

Sultan

"Sultan is a Python package for interfacing with command-line utilities, like yum, apt-get, or ls, in a Pythonic manner. It lets you run command-line utilities using simple function calls.

Sultan allows you to interface with command-line utilities from Python without having to write your scripts in Bash.

Sultan was designed because Bash just does not scale well with most projects. As much as we can try, Bash does not have a proper package management system. So, we’re left with script files that are sourced, and called, but it gets quiet complicated as soon as you have more than 10 scripts.

Python has a great package and module system that allows us to create complex projects using the Python language, but also leverage a lot of great tools and functionality that we’ve grown to love and expect from Bash."

https://sultan.readthedocs.io/en/latest/

IRPF90

"IRPF90 is a Fortran90 preprocessor written in Python for programming using the Implicit Reference to Parameters (IRP) method. It simplifies the development of large fortran codes in the field of scientific high performance computing.

IRPF90 is a Fortran programming environment which helps the development of large Fortran codes by applying the Implicit Reference to Parameters method (IRP).

In Fortran programs, the programmer has to focus on the order of the instructions: before using a variable, the programmer has to be sure that it has already been computed in all possible situations. For large codes, it is common source of error.

In IRPF90 most of the order of instructions is handled by the pre-processor, and an automatic mechanism guarantees that every entity is built before being used. This mechanism relies on the {needs/needed by} relations between the entities, which are built automatically.

Codes written with IRPF90 execute often faster than Fortran programs, are faster to write and easier to maintain."

https://github.com/scemama/irpf90

http://irpf90.ups-tlse.fr/index.php?title=IRPF90

https://scemama.gitbooks.io/irpf90/

CFortranTranslator

"A translator from Fortran90/Fortran77(ISO/IEC 1539:1991) to C++.

Fortran is an efficient tool in scientific calculation. However sometimes translating old fortran codes to C++ will enable more programming abstraction, better GUI framework support, higher performance IDE and easier interaction.

This translator is not intended to improve existing codes, but to make convenience for those who need features of C++ and remain fortran traits as much as possible."

https://github.com/CalvinNeo/CFortranTranslator

BLIS

"BLIS is a portable software framework for instantiating high-performance BLAS-like dense linear algebra libraries. The framework was designed to isolate essential kernels of computation that, when optimized, immediately enable optimized implementations of most of its commonly used and computationally intensive operations. BLIS is written in ISO C99 and available under a new/modified/3-clause BSD license. While BLIS exports a new BLAS-like API, it also includes a BLAS compatibility layer which gives application developers access to BLIS implementations via traditional BLAS routine calls.

For a thorough presentation of our framework, please read our recently accepted journal article, "BLIS: A Framework for Rapidly Instantiating BLAS Functionality". For those who just want an executive summary, please see the next section.

In a follow-up article, "The BLIS Framework: Experiments in Portability", we investigate using BLIS to instantiate level-3 BLAS implementations on a variety of general-purpose, low-power, and multicore architectures.

An IPDPS'14 conference paper titled "Anatomy of High-Performance Many-Threaded Matrix Multiplication" systematically explores the opportunities for parallelism within the five loops that BLIS exposes in its matrix multiplication algorithm.

It is our belief that BLIS offers substantial benefits in productivity when compared to conventional approaches to developing BLAS libraries, as well as a much-needed refinement of the BLAS interface, and thus constitutes a major advance in dense linear algebra computation. While BLIS remains a work-in-progress, we are excited to continue its development and further cultivate its use within the community.

BLIS offers several advantages over traditional BLAS libraries:

• Portability that doesn't impede high performance. Portability was a top priority of ours when creating BLIS. With zero additional effort on the part of the developer, BLIS is configurable as a fully-functional reference implementation. But more importantly, the framework identifies and isolates a key set of computational kernels which, when optimized, immediately and automatically optimize performance across virtually all level-2 and level-3 BLIS operations. In this way, the framework acts as a productivity multiplier. And since the optimized (non-portable) code is compartmentalized within these few kernels, instantiating a high-performance BLIS library on a new architecture is a relatively straightforward endeavor.
  
• Generalized matrix storage. The BLIS framework exports interfaces that allow one to specify both the row stride and column stride of a matrix. This allows one to compute with matrices stored in column-major order, row-major order, or by general stride. (This latter storage format is important for those seeking to implement tensor contractions on multidimensional arrays.) Furthermore, since BLIS tracks stride information for each matrix, operands of different storage formats can be used within the same operation invocation. By contrast, BLAS requires column-major storage. And while the CBLAS interface supports row-major storage, it does not allow mixing storage formats.
  
• Full support for the complex domain. BLIS operations are developed and expressed in their most general form, which is typically in the complex domain. These formulations then simplify elegantly down to the real domain, with conjugations becoming no-ops. Unlike the BLAS, all input operands in BLIS that allow transposition and conjugate-transposition also support conjugation (without transposition), which obviates the need for thread-unsafe workarounds. Also, where applicable, both complex symmetric and complex Hermitian forms are supported. (BLAS omits some complex symmetric operations, such as symv, syr, and syr2.)
  
• Advanced multithreading support. BLIS allows multiple levels of symmetric multithreading for nearly all level-3 operations. (Currently, users may choose to obtain parallelism via either OpenMP or POSIX threads). This means that matrices may be partitioned in multiple dimensions simultaneously to attain scalable, high-performance parallelism on multicore and many-core architectures. The key to this innovation is a thread-specific control tree infrastructure which encodes information about the logical thread topology and allows threads to query and communicate data amongst one another. BLIS also employs so-called "quadratic partitioning" when computing dimension sub-ranges for each thread, so that arbitrary diagonal offsets of structured matrices with unreferenced regions are taken into account to achieve proper load balance.
  
• Ease of use. The BLIS framework, and the library of routines it generates, are easy to use for end users, experts, and vendors alike. An optional BLAS compatibility layer provides application developers with backwards compatibility to existing BLAS-dependent codes. Or, one may adjust or write their application to take advantage of new BLIS functionality (such as generalized storage formats or additional complex operations) by calling BLIS directly. BLIS's interfaces will feel familiar to many veterans of BLAS since BLIS exports APIs with BLAS-like calling sequences. And experts will find BLIS's internal object-based APIs a delight to use when customizing or writing their own BLIS operations. (Objects are relatively lightweight structs and passed by address, which helps tame function calling overhead.)
  
• Multilayered API and exposed kernels. The BLIS framework exposes its implementations in various layers, allowing expert developers to access exactly the functionality desired. This layered interface includes that of the lowest-level kernels, for those who wish to bypass the bulk of the framework. Optimizations can occur at various levels, in part thanks to exposed packing and unpacking facilities, which by default are highly parameterized and flexible.
  
• Functionality that grows with the community's needs. As its name suggests, the BLIS framework is not a single library or static API, but rather a nearly-complete template for instantiating high-performance BLAS-like libraries. Furthermore, the framework is extensible, allowing developers to leverage existing components to support new operations as they are identified. If such operations require new kernels for optimal efficiency, the framework and its APIs will be adjusted and extended accordingly.
  
• Code re-use. Auto-generation approaches to achieving the aforementioned goals tend to quickly lead to code bloat due to the multiple dimensions of variation supported: operation (i.e. gemm, herk, trmm, etc.); parameter case (i.e. side, [conjugate-]transposition, upper/lower storage, unit/non-unit diagonal); datatype (i.e. single-/double-precision real/complex); matrix storage (i.e. row-major, column-major, generalized); and algorithm (i.e. partitioning path and kernel shape). These "brute force" approaches often consider and optimize each operation or case combination in isolation, which is less than ideal when the goal is to provide entire libraries. BLIS was designed to be a complete framework for implementing basic linear algebra operations, but supporting this vast amount of functionality in a manageable way required a holistic design that employed careful abstractions, layering, and recycling of generic (highly parameterized) codes, subject to the constraint that high performance remain attainable.
  
• A foundation for mixed domain and/or mixed precision operations. BLIS was designed with the hope of one day allowing computation on real and complex operands within the same operation. Similarly, we wanted to allow mixing operands' floating-point precisions, or both domain and precision. Unfortunately, this feature results in a significant amount of additional code, mostly in level-2 and lower operations, thus, it is disabled by default. However, mixing domains in level-3 operations is possible, in theory, with almost no additional effort on the part of the library developer, and such operations would remain capable of high performance. (Please note that this functionality is still highly experimental and should be thought of as a feature that will be more thoroughly implemented at some future date.)

https://github.com/amd/blis

PyCall.jl

"This package provides the ability to directly call and fully interoperate with Python from the Julia language. You can import arbitrary Python modules from Julia, call Python functions (with automatic conversion of types between Julia and Python), define Python classes from Julia methods, and share large data structures between Julia and Python without copying them."

https://github.com/JuliaPy/PyCall.jl