Use Of Dynamic Memory Research Articles

Restrictive language semantics in the Multioberon system

The Oberon-based language and systems in implementation demonstrate a minimalist approach to achieving reliability, significantly different from most software systems that seek to maximize the number of supported functions. The requirements for critical systems for Category A nuclear power plants prohibit the use of even more programming practices. In order to meet the category A requirement of a stable number of iterations, the use of conditional loop operators is prohibited. To ensure ergodicity, the prohibition of the use of dynamic memory and recursion is used. A buffer overflow type vulnerability is closed by prohibiting the system operations module SYSTEM. Restrictions can be set to identify the problem of a fragile base class, type change operations, and the use of nested procedures. It is noted that the transition to the Oberon-07 dialect mainly concerned additional restrictions and fits well into the framework of restrictive semantics. Instead of languages and dialects for each set of requirements, the author proposes an approach of restrictive semantics, in which one language with a system of restrictions is used. A single RESTRICT statement has been introduced into the language as a declaration of restrictions on this module. The Multioberon compiler is implemented with one frontend, including a system of restrictions, and several replaceable backends. The syntactic analysis of the compiler is demonstrated by examples. The strategy of scaling the compiler depending on the system requirements is shown. The novelty of the restrictive semantics approach is the achievement of a set of minimum necessary properties that meet the requirements for the system. The use of the "from limitations" approach by system developers is an advantage, because it declares the really necessary properties of the system, linked to the requirements.

Auto-Regressive Neural-Network Models for Long Lead-Time Forecasting of Daily Flow

Accurate reservoir-inflow forecasting is especially important for optimizing operation of multi-propose reservoirs that provide hydropower generation, flood control, and water for domestic use and irrigation. There are no previous reports of successful daily flow prediction using a 1-year lead-time. This paper reports successful daily stream flow predictions for that extended lead-time. It presents the first NARX (Nonlinear Auto Regressive model with eXogenous inputs)-type recurrent neural network (NARX-RNN) model used to forecast daily reservoir inflow for a long lead-time. It is the first use of dynamic memory to extend the forecast lead-time beyond the previously reported 1-week lead-times. For new nonlinear NARX-RNN models, we present and test 1600 alternative structures, differing in transfer functions (2), and numbers of inputs (2 to 5), neurons per hidden layer (1 to 20), input delays and output delays. For predicting inflow to the reservoir of the multi-purpose Dez Dam, we contrast accuracies of forecasts from the new models, and from a conventional auto-regressive linear ARIMA model. Based upon normalized root-mean-square error $$ \mathrm{RMSE}/{\overline{Q}}_{obs} $$ the best NARX-RNN has log-sigmoid transfer functions, three inputs, one hidden layers, four neurons in the hidden layer, two input delays, and 10 output delays. That NARX-RNN structure yields $$ \mathrm{RMSE}/{\overline{Q}}_{obs} $$ values of 0.616 in training and 0.678 in forecasting. The proposed model’s forecasting $$ \mathrm{RMSE}/{\overline{Q}}_{obs} $$ is 20% lower than that of the ARIMA model.

International symposium of memory management (ISMM 2008)

The ACM SIGPLAN International Symposium on Memory Management (ISMM) is a premier forum for research in the management of dynamically allocated memory. We interpret this remit widely: areas of interest include but are not limited to explicit storage allocation and deallocation; garbage collection algorithms and implementations; compiler analyses to aid memory management; interactions with languages, operating systems and hardware (especially the memory system); memory management related tools; and empirical studies of allocation and referencing behavior in programs that make significant use of dynamic memory.