Theano: new features and speed improvements

Theano is a linear algebra compiler that optimizes a user's symbolically-specified mathematical computations to produce efficient low-level implementations. In this paper, we present new features and efficiency improvements to Theano, and benchmarks demonstrating Theano's performance relative to Torch7, a recently introduced machine learning library, and to RNNLM, a C++ library targeted at recurrent neural networks.Comment: Presented at the Deep Learning Workshop, NIPS 201

A Domain-Specific Language and Editor for Parallel Particle Methods

Domain-specific languages (DSLs) are of increasing importance in scientific high-performance computing to reduce development costs, raise the level of abstraction and, thus, ease scientific programming. However, designing and implementing DSLs is not an easy task, as it requires knowledge of the application domain and experience in language engineering and compilers. Consequently, many DSLs follow a weak approach using macros or text generators, which lack many of the features that make a DSL a comfortable for programmers. Some of these features---e.g., syntax highlighting, type inference, error reporting, and code completion---are easily provided by language workbenches, which combine language engineering techniques and tools in a common ecosystem. In this paper, we present the Parallel Particle-Mesh Environment (PPME), a DSL and development environment for numerical simulations based on particle methods and hybrid particle-mesh methods. PPME uses the meta programming system (MPS), a projectional language workbench. PPME is the successor of the Parallel Particle-Mesh Language (PPML), a Fortran-based DSL that used conventional implementation strategies. We analyze and compare both languages and demonstrate how the programmer's experience can be improved using static analyses and projectional editing. Furthermore, we present an explicit domain model for particle abstractions and the first formal type system for particle methods.Comment: Submitted to ACM Transactions on Mathematical Software on Dec. 25, 201

Dynamic Control Flow in Large-Scale Machine Learning

Many recent machine learning models rely on fine-grained dynamic control flow for training and inference. In particular, models based on recurrent neural networks and on reinforcement learning depend on recurrence relations, data-dependent conditional execution, and other features that call for dynamic control flow. These applications benefit from the ability to make rapid control-flow decisions across a set of computing devices in a distributed system. For performance, scalability, and expressiveness, a machine learning system must support dynamic control flow in distributed and heterogeneous environments. This paper presents a programming model for distributed machine learning that supports dynamic control flow. We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system. Our approach extends the use of dataflow graphs to represent machine learning models, offering several distinctive features. First, the branches of conditionals and bodies of loops can be partitioned across many machines to run on a set of heterogeneous devices, including CPUs, GPUs, and custom ASICs. Second, programs written in our model support automatic differentiation and distributed gradient computations, which are necessary for training machine learning models that use control flow. Third, our choice of non-strict semantics enables multiple loop iterations to execute in parallel across machines, and to overlap compute and I/O operations. We have done our work in the context of TensorFlow, and it has been used extensively in research and production. We evaluate it using several real-world applications, and demonstrate its performance and scalability.Comment: Appeared in EuroSys 2018. 14 pages, 16 figure

Derivation of Einstein Cartan theory from general relativity

This work derives the elements of classical Einstein Cartan theory (EC) from classical general relativity (GR) in two ways. (I) Derive translational holonomy and the spin torsion field equation of EC for one Kerr mass in GR. (II) Derive the field equations of EC as the continuum limit of a sequence of discrete distributions of Kerr masses in classical GR with no electric charge. his derivation does not extend to the quantum domain because of inequality constraints. The convergence computations employ epsilon delta arguments, and are not as rigorous as weak convergence in Sobolev norm. Derivation of EC from GR strengthens the case for new physics derived from EC, including modeling exchange of intrinsic and orbital angular momentum, removing some gravitational singularities from Big Bang and black hole models, introducing a spin contact force that is a geometric candidate for the origin of cosmic inflation, and providing a better classical limit for theories of quantum gravity.Comment: 47 pages, 1 table, 66 equations, 3 figures, 93 lines of computer algebra, 33 references. This version improves organization and many sections. It argues that deriving EC from GR greatly strengthens the case for new physics that is derived from EC; the new physics is listed. Section 2 updates the 1986 paper below. Petti RJ, 1986, Gen Rel Grav vol 18, 441-46

Stability and mode analysis of solar coronal loops using thermodynamic irreversible energy principles

We study the modes and stability of non - isothermal coronal loop models with different intensity values of the equilibrium magnetic field. We use an energy principle obtained via non - equilibrium thermodynamic arguments. The principle is expressed in terms of Hermitian operators and allow to consider together the coupled system of equations: the balance of energy equation and the equation of motion. We determine modes characterized as long - wavelength disturbances that are present in inhomogeneous media. This character of the system introduces additional difficulties for the stability analysis because the inhomogeneous nature of the medium determines the structure of the disturbance, which is no longer sinusoidal. Moreover, another complication is that we obtain a continuous spectrum of stable modes in addition to the discrete one. We obtain a unique unstable mode with a characteristic time that is comparable with the characteristic life-time observed for loops. The feasibility of wave-based and flow-based models is examined.Comment: 29 pages 10 figure

Mathematical Modeling of the Mojave Solar Plants

Competitiveness of solar energy is one of current main research topics. Overall efficiency of solar plants can be improved by using advanced control strategies. To design and tuning properly advanced control strategies, a mathematical model of the plant is needed. The model has to fulfill two important points: (1) It has to reproduce accurately the dynamics of the real system; and (2) since the model is used to test advanced control strategies, its computational burden has to be as low as possible. This trade-off is essential to optimize the tuning process of the controller and minimize the commissioning time. In this paper, the modeling of the large-scale commercial solar trough plants Mojave Beta and Mojave Alpha is presented. These two models were used to test advanced control strategies to operate the plants.Comisión Europea OCONTSOLAR 78905