THE CONCEPTION AND APPLICATION OF PFL : A PROCESS\ud FUNCTIONAL PROGRAMMING LANGUAGE

A new process functional programming paradigm and its application in PFL –\ud a process functional programming language is introduced in the paper. This paradigm is\ud based on affecting the state represented by the values of memory cells strictly by\ud evaluating expressions. Process functional conception prevents the use of assignments,\ud which is a good basis for reasoning about the programs and systems by direct PFL program\ud profiling and transformation. This paper is oriented to essential constructs of PFL language\ud and its relation to object and parallel programming paradigm

Differentiable Programming Tensor Networks

Differentiable programming is a fresh programming paradigm which composes parameterized algorithmic components and trains them using automatic differentiation (AD). The concept emerges from deep learning but is not only limited to training neural networks. We present theory and practice of programming tensor network algorithms in a fully differentiable way. By formulating the tensor network algorithm as a computation graph, one can compute higher order derivatives of the program accurately and efficiently using AD. We present essential techniques to differentiate through the tensor networks contractions, including stable AD for tensor decomposition and efficient backpropagation through fixed point iterations. As a demonstration, we compute the specific heat of the Ising model directly by taking the second order derivative of the free energy obtained in the tensor renormalization group calculation. Next, we perform gradient based variational optimization of infinite projected entangled pair states for quantum antiferromagnetic Heisenberg model and obtain start-of-the-art variational energy and magnetization with moderate efforts. Differentiable programming removes laborious human efforts in deriving and implementing analytical gradients for tensor network programs, which opens the door to more innovations in tensor network algorithms and applications.Comment: Typos corrected, discussion and refs added; revised version accepted for publication in PRX. Source code available at https://github.com/wangleiphy/tensorgra

Separating Concerns in Programming: Data, Control and Actions

A multi-paradigm language provides an opportunity to a user for exploiting more programming methodologies. It simplifies the language syntax, and extends the application areas by the extended semantics. That is why multi-paradigm languages can align a problem in wider application areas and more flexibly than that based on a single paradigm. In this paper, we present the idea of separating three essential concerns of programming currently being implemented in PFL -- a process functional language. We separate data, control, and actions by the definition of a purely control structure. Then, by the structured application of a structure of actions to a purely control structure, we will express the computation of activated actions in a structured way, considering explicitly defined synchronization in computation

Analysis, classification and comparison of scheduling techniques for software transactional memories

Transactional Memory (TM) is a practical programming paradigm for developing concurrent applications. Performance is a critical factor for TM implementations, and various studies demonstrated that specialised transaction/thread scheduling support is essential for implementing performance-effective TM systems. After one decade of research, this article reviews the wide variety of scheduling techniques proposed for Software Transactional Memories. Based on peculiarities and differences of the adopted scheduling strategies, we propose a classification of the existing techniques, and we discuss the specific characteristics of each technique. Also, we analyse the results of previous evaluation and comparison studies, and we present the results of a new experimental study encompassing techniques based on different scheduling strategies. Finally, we identify potential strengths and weaknesses of the different techniques, as well as the issues that require to be further investigated

The Conception and Application of PFL: a Process Functional Programming Language

A new process functional programming paradigm and its application in PFL – a process functional programming language is introduced in the paper. This paradigm is based on affecting the state represented by the values of memory cells strictly by evaluating expressions. Process functional conception prevents the use of assignments, which is a good basis for reasoning about the programs and systems by direct PFL program profiling and transformation. This paper is oriented to essential constructs of PFL language and its relation to object and parallel programming paradigm

A formal semantics for control and data flow in the gannet service-based system-on-chip architecture

There is a growing demand for solutions which allow the design of large and complex reconfigurable Systems-on- Chip (SoC) at high abstraction levels. The Gannet project proposes a functional programming approach for high-abstraction design of very large SoCs. Gannet is a distributed service-based SoC architecture, i.e. a network of services offered by hardware or software cores. The Gannet SoC is task-level reconfigurable: it performs tasks by executing functional task description programs using a demand-driven dataflow mechanism. The Gannet architecture combines the flexible connectivity offered by a Networkon- Chip with the functional language paradigm to create a fully concurrent distributed SoC with the option to completely separate data flows from control flows. This feature is essential to avoid a bottleneck at he controller for run-time control of multiple high-throughput data flows. In this paper we present the Gannet architecture and language and introduce an operational semantics to formally describe the mechanism to separate control and data flows