SMARQ: Software-Managed Alias Register Queue for Dynamic Optimizations

Dynamic optimization has the potential to adapt the program's behavior at run-time to deliver performance improvements over static optimization. Dynamic optimization systems usually perform their optimization in series with the application's execution. This incurs overhead which reduces the benefit of dynamic optimization, and prevents some aggressive optimizations from being performed. In this paper we propose a new dynamic optimization framework called Trident. Concurrent with the program's execution, the framework uses hardware support to identify optimization opportunities, and uses spare threads on a multithreaded processor to perform dynamic optimizations for these optimization events. We evaluate the benefit of using Trident to guide code layout, basic compiler optimizations, and value specialization. Our results show that using Trident with these optimizations achieves an average 20% speedup, and is complementary with other memory latency tolerant techniques, such as prefetching.

A dynamic optimizer is a runtime software system that groups a program's instruction sequences into traces, optimizes those traces, stores the optimized traces in a software-based code cache, and then executes the optimized code in the code cache. To maximize performance, the vast majority of the program's execution should occur in the code cache and not in the different aspects of the dynamic optimization system. In the past, designers of dynamic optimizers have used the SPEC2000 benchmark suite to justify their use of simple code cache management schemes. In this paper, we show that the problem and importance of code cache management changes dramatically as we move from SPEC2000, with its relatively small number of dynamically generated code traces, to large interactive Windows applications. We also propose and evaluate a new cache management algorithm based on generational code caches that results in an average miss rate reduction of 18% over a unified cache, which translates into 19% fewer instructions spent in the dynamic optimizer. The algorithm categorizes code traces based on their expected lifetimes and group traces with similar lifetimes together in separate storage areas. Using this algorithm, short-lived code traces can easily be removed from a code cache without introducing fragmentation and without suffering the performance penalties associated with evicting long-lived code traces.

The power systems of the future - smart grid - will see an increase in both renewable energy sources and load demand increasing the need for fast dynamic reconfiguration of system parameters. Advanced simulation capabilities are needed to support future planning and to implement real-time dynamic optimization. To achieve this, intelligent algorithms are needed for prediction and system monitoring along with advanced hardware to provide speed. FASTSIM capabilities allows for fast prediction of how a proposed reconfiguration will affect the system permitting implementation of radical reconfigurations without fear of disturbing vital system settings since the proposed solution will be checked before being implemented. Power systems will need to be continuously monitored for power losses and system contingencies. A combination of system states prediction, dynamic load flow, system optimization, and solution stability checking will allow for a more reliable, affordable and clean power grid. This paper presents a dynamic optimization method based on adaptive critic designs for the smart grid environment.

This paper demonstrates that ant colony optimization can efficiently improve the traffic situation in an urban environment. A Dynamic Travel Path Optimization System (DTPOS) based on Ant Colony Optimization (ACO) is proposed for the prediction of the best path to a given destination. In DTPOS, traffic factors such as average travel speed, average waiting time of cars and number of stopped cars in queue are taken into consideration. The proposed method is modeled in Net Logo. The simulation results demonstrate that the DTPOS model can greatly reduce the average travel time of cars in urban cases and improves the mean travel time by 47 percent when compared to similar models where the cars select their path without ACO. It has also been shown that the results can be further improved by 56 percent when the Previous Path Replacement (PPR) method is applied to the DTPOS results.

— A gradient learning method to regulate the trajectories of some nonlinear chaotic systems is proposed. The method is motivated by the gradient descent learning algorithms for neural networks. It is based on two systems: dynamic optimization system and system for finding sensitivities. Numerical results of several examples are presented, which convincingly illustrate the efficiency of the method. Keywords — Chaos, Dynamical Systems, Learning, Neural Networks I. I NTRODUCTION H E study of controlling chaos in non-linear deterministic systems has been in focus during the last two decades. The ability to bring chaotic dynamical systems to regular motions is an important subject. The existing chaos control algorithms can be classified mainly into two categories: feed-back methods [1 - 4] and non-feedback methods [5, 6]. In [7, 8] the authors showed that adding noise to the excitation frequency of the Duffing system the initially chaotic motion becomes regular. In the present paper we propose a gradient learning method for adjusting the system parameters so that the trajectory of the system has certain specified properties. In the theory of neural networks the gradient descent learning requires that the performance of a dynamical system is assessable through certain error function which measures the discrepancy between the trajectories of the dynamical system and the desired behavior [9]. During gradient learning the interconnection weights between neurons are iteratively adjusted to reduce the error. In the present work we consider the system coefficients as adjustable parameters to obtain the desired behavior of the dynamical system. II. PROBLEMSTATEMENTConsider the dynamical system F u w I u t

Aiming at the vehicle power train, system dynamic optimization is profoundly studied based on the system torsional vibration characteristics analysis. First, based on the concentrated mass method, the general torsional vibration model of vehicle power train is established and solved after parameters’ (inertia, stiffness, and damping) matrix and mathematic constraint conditions are acquired. Furthermore, both free vibration and forced vibration characteristics are analyzed. Second, the effects of the coupling stiffness on the dynamics behaviors of power train are thoroughly analyzed. The sensitivity analysis procedure is explored. And sensitivity models of both free and forced vibration feature parameters are deduced. Finally, dynamic optimization theory model and program are constructed based on genetic algorithm. The optimization results indicate that the proposed optimization method could contribute to the sharp attenuation of system torsional vibration.

Assessing the consequences of exceptional events, such as failures and sudden changes in product demand or raw‐material prices, is of great importance to the operation of chemical processes. Such an exceptional event and the resulting behavior of the process are called a scenario in this article. Problem formulations for their rigorous incorporation into the optimization of dynamic systems are presented. They allow the determination of operational strategies that are economically optimal and that keep the plant within a chosen regime in the state space, even for a number of possible scenarios. The formulations are based on a unifying model framework containing both continuous and discrete dynamics. For a restricted class of problems a method for the numerical solution of the resulting scenario‐integrated dynamic optimization problems is introduced. The applicability of this technique is demonstrated with two examples of different complexity.

Future computer systems will integrate tens of multithreaded processor cores on a single chip die, resulting in hundreds of concurrent program threads sharing system resources. These designs will be the cornerstone of improving throughput in high-performance computing and server environments. However, to date, appropriate systems software (operating system, run-time system, and compiler) technologies for these emerging machines have not been adequately explored. Future processors will require sophisticated hardware monitoring units to continuously feed back resource utilization information to allow the operating system to make optimal thread co-scheduling decisions and also to software that continuously optimizes the program itselfNevertheless, in order to continually and automatically adapt systems resources to program behaviors and application needs, specific run-time information must be collected to adequately enable dynamic code optimization and operating system scheduling. Generally, run-time optimization is limited by the time required to collect profiles, the time required to perform optimization, and the inherent benefits of any optimization or decisions. Initial techniques for effectively utilizing run-time information for dynamic optimization and informed thread scheduling in future multithreaded architectures are presented

Optimization of dynamic control systems using water cycle algorithm

We present here an uncertainty quantification methodology assisting fracture/damage detection that accounts for different sources of uncertainty involving in real-time damage identification. The relationship between structural responses (i.e., the natural frequencies) and the uncertain crack locations and lengths in mechanical systems is quantitatively assessed using variance-based sensitivity analysis. Sparse variational Gaussian process is employed to replace the computationally expensive finite element model. Dynamic behaviors of two mechanical systems which are (1) aluminum frame with adjustable masses and (2) cracked car suspension arm are analyzed through experimental and numerical data. The results demonstrate the effectiveness and robustness of combining these methods for structural assessments, providing precise and reliable insights applicable to complex mechanical models. We believe that this study enhances the understanding of vibration-induced fracture/damage and highlights the potential of this framework for advancing structural reliability analysis and dynamic system optimization.

This paper presents an algorithm that allocates registers optimally for straight-line code running on a generic multi-issue computer. On such a machine, an optimal register allocation is one that minimizes the number of issue slots that the code requires. Optimal spill selection and load/store placement are used to minimize the number of additional issue slots needed, given a schedule for the non-memory reference instructions and a fixed number of available physical registers. The generic multi-issue machine model closely models the operation of vector and VLIW processors, and could be extended to model super-scalar processors. The algorithm uses dynamic programming to search the state space of feasible register allocations; implicit and explicit state pruning are used to make the problem tractable without sacrificing optimality. The optimal allocation produced by the algorithm for a substantial example is presented.

Coal blending has become an important way to ease the tension of coal purchase for many Chinese power plants. Mixed by pulverizers which has been widely used, is considered the most reasonable and convenient approach of coal blending. The implementation of this approach meet three main problems, that is to determine the optimum coal blending formula, to select the pulverizer combination for single coals and to allocate the outputs of pulverizers. Previous scholars have done a lot research on the model and algorithm of the first problem, but the latter two which were lack of theoretical guidance, rely mainly on experience in the application. To remedy this situation, a novel model of pulverizer optimization based on new indicators characterizing the burnout and slagging characteristics, the property of stable combustion at low load and pollutant emissions characteristics among different pulverizer combinations, was proposed in this paper. The model contains the pulverizer-coal combination optimization and optimum output of each pulverizer, which are solved by exhaustive algorithm and non-dominated sorting genetic algorithm II(NSGA-II) respectively. It has now been successfully applied in the Dynamic Coal Blending Optimization System of Guangdong Red Bay Power Plant.

Program optimizations based on data dependences may not preserve the memory consistency in the programs. Previous works leverage a hardware ATOMICITY primitive to restrict the thread interleaving for preserving sequential consistency in region optimizations. However, ATOMICITY primitive is over restrictive on the thread interleaving for optimizing real-world applications developed with the popular Total-Store-Ordering (TSO) memory consistency, which is weaker than sequential consistency. In this paper, we present a novel hardware TSO_ATOMICITY primitive, which has less restriction on the thread interleaving than ATOMICITY primitive to permit more efficient program execution than ATOMICITY primitive, but can still preserve TSO memory consistency in all region optimizations. Furthermore, TSO_ATOMICITY primitive requires similar architecture support as ATOMICITY primitive and can be implemented with only slight change to the existing ATOMICITY primitive implementation. Our experimental results show that in a start-of-art dynamic binary optimization system on a large set of workloads, ATOMICITY primitive can only improve the performance by 4% on average. TSO_ATOMICITY primitive can reduce the overhead associated with ATOMICITY primitive and improve the performance by 12% on average.

Both dynamic binary translation systems and optimization systems store the translated or optimized code in the software maintained code cache for reuse. The performance of the code cache is crucial. Translated code is usually organized as code blocks in the code cache and each code block transfer control to the next one through a control transfer instruction. As the target address of a control transfer instruction is in the form of its source program counter, the conventional code cache system has to check the address mapping table for the translated target address to find the required target code block, which will cause considerable performance degradation. Control transfer instructions can be divided into two categories as direct control transfer instructions and indirect control transfer instructions. For indirect control transfer instructions, the target address is hold in the register or memory element whose content can be changed during the execution of the program. It is difficult to chain the indirect control transfer instructions with a fixed translated target address through pure software approaches. A novel indirect control transfer chaining approach is proposed in this paper. The principle of the technique is to insert custom chaining instructions into the translated code block while translating the indirect control transfer instructions and execute those chaining instructions to implement dynamical chaining. Some special hardware and software assists are proposed in this paper. Evaluation of the proposed approach is conducted on a code cache simulator. Experiment results show that our hardware assisted indirect control transfer instruction chaining approach can improve the performance of the code cache system dramatically.Keywordscode cacheindirect control transfer instructiondynamic chainingsimulator

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation (DISOPE) was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.