A Parallel Evolutionary Algorithm to Optimize Dynamic Data Types in Embedded Systems

New multimedia embedded applications are increasingly dynamic, and rely on dynamically-allocated data types (DDTs) to store their data. The optimization of DDTs for each target embedded system is a time-consuming process due to the large searching space of possible DDTs implementations. That implies the minimization of embedded design variables (memory accesses, power consumption and memory usage). Up to know, some very effective heuristic algorithms have been developed in order to solve this problem, but it is unknown how good the selected DDTs are since the problem is NP-complete and cannot be fully explored. In these cases the use of parallel processing can be very useful because it allows not only to explore more solutions spending the same time, but also to implement new algorithms. This paper describes several parallel evolutionary algorithms for DDTs optimization in Embedded Systems, where parallelism improves the solutions found by the corresponding sequential algorithm, which indeed is quite effective compared with other previously proposed procedures. Experimental results show how a novel parallel multi-objective genetic algorithm, which combines NSGA-II and SPEA2, allows designers to reach a larger number of solutions than previous approximations

Simulation of High-Performance Memory Allocators

Current general-purpose memory allocators do not provide sufficient speed or flexibility for modern highperformance applications. To optimize metrics like performance, memory usage and energy consumption, software engineers often write custom allocators from scratch, which is a difficult and error-prone process. In this paper, we present a flexible and efficient simulator to study Dynamic Memory Managers (DMMs), a composition of one or more memory allocators. This novel approach allows programmers to simulate custom and general DMMs, which can be composed without incurring any additional runtime overhead or additional programming cost. We show that this infrastructure simplifies DMM construction, mainly because the target application does not need to be compiled every time a new DMM must be evaluated. Within a search procedure, the system designer can choose the "best" allocator by simulation for a particular target application. In our evaluation, we show that our scheme will deliver better performance, less memory usage and less energy consumption than single memory allocators

A Parallel Evolutionary Algorithm to Optimize Dynamic Memory Managers in Embedded Systems

For the last 30 years, several dynamic memory managers (DMMs) have been proposed. Such DMMs include first fit, best fit, segregated fit and buddy systems. Since the performance, memory usage and energy consumption of each DMM differs, software engineers often face difficult choices in selecting the most suitable approach for their applications. This issue has special impact in the field of portable consumer embedded systems, that must execute a limited amount of multimedia applications (e.g., 3D games, video players, signal processing software, etc.), demanding high performance and extensive memory usage at a low energy consumption. Recently, we have developed a novel methodology based on genetic programming to automatically design custom DMMs, optimizing performance, memory usage and energy consumption. However, although this process is automatic and faster than state-of-the-art optimizations, it demands intensive computation, resulting in a time-consuming process. Thus, parallel processing can be very useful to enable to explore more solutions spending the same time, as well as to implement new algorithms. In this paper we present a novel parallel evolutionary algorithm for DMMs optimization in embedded systems, based on the Discrete Event Specification (DEVS) formalism over a Service Oriented Architecture (SOA) framework. Parallelism significantly improves the performance of the sequential exploration algorithm. On the one hand, when the number of generations are the same in both approaches, our parallel optimization framework is able to reach a speed-up of 86.40% when compared with other state-of-the-art approaches. On the other, it improves the global quality (i.e., level of performance, low memory usage and low energy consumption) of the final DMM obtained in a 36.36% with respect to two well-known general-purpose DMMs and two state-of-the-art optimization methodologies

Simulation of High-Performance Memory Allocators

Current general-purpose memory allocators do not provide sufficient speed or flexibility for modern highperformance applications. To optimize metrics like performance, memory usage and energy consumption, software engineers often write custom allocators from scratch, which is a difficult and error-prone process. In this paper, we present a flexible and efficient simulator to study Dynamic Memory Managers (DMMs), a composition of one or more memory allocators. This novel approach allows programmers to simulate custom and general DMMs, which can be composed without incurring any additional runtime overhead or additional programming cost. We show that this infrastructure simplifies DMM construction, mainly because the target application does not need to be compiled every time a new DMM must be evaluated. Within a search procedure, the system designer can choose the "best" allocator by simulation for a particular target application. In our evaluation, we show that our scheme will deliver better performance, less memory usage and less energy consumption than single memory allocator

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

A descriptive study of selected trumpet concertos from the Soviet Union

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