Genetic algorithm for mapping tasks onto a reconfigurable parallel processor

Ant colony optimization is an evolutionary approach where a number of ants search for good solutions. Ant Algorithms often show good optimization results. This paper, discusses number of algorithms built using modified ant colony algorithm which are used to solve the problem of assigning each task in a given task graph to a processor in a reconfigurable multi processor architecture so as to minimize the total overall execution time of the tasks. Each group of ants is working collectively to find the interconnection architecture best suited for the task graph and the best scheduling of that task graph on the chosen interconnection architecture. The total execution time is computed according to the knowledge of individual run times of tasks and the communication requirements among tasks. The inter task communication time is dependent on the interconnection processor architecture. The results of using modified ant colony algorithm in reconfigurable parallel processor system are compared with that of using genetic algorithm for solving the same problem.

With the increase in the volume of container trading, automated container terminals have gradually become the target of the world’s largest port construction. In order to improve the efficiency of the automated AGV container terminal and gantry cranes, the possible conflicts in the horizontal AGV transportation area under different load conditions and speeds have been resolved. The problem is to create a comprehensive scheduling model that minimizes the total AGV execution time through prediction and solved by genetic algorithm. Experimental data analysis shows that after optimization, the total execution time and conflict timeout of multiple AGVs are reduced, which improves the work efficiency of AGVs.

This study aimed to kinematically compare the Yop Chagi execution between novice and experienced university Taekwondo practitioners, focusing on movement efficiency. Forty university students were allocated into two groups: Novice (n = 20; ≤6 months experience) and Experienced (n = 20; ≥3 years of competitive experience). Participants performed maximal effort side kicks targeting a pad at trochanter height. Movement was captured using two high-speed cameras (120 Hz). Kinematic variables included hip and knee joint angles at peak flexion/extension, peak angular velocities, linear velocity of the foot, and total kick execution time. Between-group differences were analysed using independent t-tests or Mann-Whitney U tests, with effect sizes (Cohen's d) reported. The experienced group demonstrated significantly larger hip abduction and knee flexion angles during the chamber phase (p < 0.01, d > 0.8), and greater knee extension at the point of impact (p < 0.01, d = 1.2) compared to novices. The experienced group also exhibited superior performance in peak hip and knee angular velocities (p < 0.01, d > 1.5), resulting in a 28% higher peak linear velocity of the foot (p < 0.001, d = 2.1). Furthermore, the total execution time was significantly shorter for the experienced group (p < 0.01, d = 1.4). Experienced practitioners execute the Yop Chagi with a more efficient kinematic pattern, characterized by a larger range of motion, faster segmental velocities, and reduced execution time. These findings suggest that long-term training optimizes the proximal-to-distal kinematic sequence, enhancing kick performance.

This paper addresses the problem of estimating the total execution time of a parallel program based on a domain decomposition strategy.When the execution time of each processor may vary, the total execution time is non deterministic, specially if the communication to exchange boundary data is asynchronous.We consider the situation where a single iteration on each processor can take two different execution times. We show that the total time depends on the topology of the interconnection network and provide a lower bound for the ring and the grid.This analysis is supported further by a set of simulations and comparisons of specific cases.KeywordsExecution TimeDomain DecompositionBubble SizeTotal DelayDomain Decomposition MethodThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

It is well known that innermost loop optimizations have a big effect on the total execution time. Although CGRAs is widely used for this type of optimizations, their usage at run-time has been limited due to the overheads introduced by application analysis, code transformation, and reconfiguration. These steps are normally performed during compile time. In this work, we present the first dynamic translation technique for the modulo scheduling approach that can convert binary code on-the-fly to run on a CGRA. The proposed mechanism ensures software compatibility as it supports different source ISAs. As proof of concept of scaling, a change in the memory bandwidth has been evaluated (from one memory access per cycle to two memory accesses per cycle). Moreover, a comparison to the state-of-the-art static compiler-based approaches for inner loop accelerators has been done by using CGRA and VLIW as target architectures. Additionally, to measure area and performance, the proposed CGRA was prototyped on a FPGA. The area comparisons show that crossbar CGRA (with 16 processing elements) is 1.9x larger than the VLIW 4-issue and 1.3x smaller than a VLIW 8-issue softcore processor, respectively. In addition, it reaches an overall speedup factor of 2.17x and 2.0x in comparison to the 4 and 8-issue, respectively. Our results also demonstrate that the runtime algorithm can reach a near-optimal ILP rate, better than an off-line compiler approach for an n-issue VLIW processor.

With the rapid development of in modern technologies has led to a novel generation of cyberspace equipment and secure analysis methods such as cyber‐physical system (CPS). The CPS system refers to an intelligent dependable system of embedded smart devices used to monitor and control the cyberspace conditions in a fog computing environment. The fog computing environment connects resources even human beings together to enhance the quality of life through performing of CPS applications using computation resources at the network edge. The optimization of task scheduling for the CSP applications as a challenging issue has been considered as the NP‐hard problem in fog computing environment which is not trivial. On the one hand, reasonable tasks scheduling can increase resource utilization, which can avoid less idle resource. This paper presents a task scheduling algorithm based on moth‐flame optimization algorithm to assign an optimal set of tasks to fog nodes to meet the satisfaction of quality of service requirements of CPS applications in such a way that the total execution time of tasks is minimized. The minimization of task execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed solution is carried out in iFogSim toolkit. As a result of the simulation based on proposed algorithm found, the optimal solution for the scheduling of tasks and equal distribution of tasks to fog nodes has been provided, and less total execution time consumption has been achieved compared with other algorithms.

Adaptive routing is widely regarded as a promising approach to improving interconnection network performance. Many designers of adaptive routing algorithms have used synthetic communication patterns, such as uniform and transpose traffic, to compare the performance of various adaptive routing algorithms with each other and with oblivious routing. These comparisons have shown that the average message latency is usually lower with adaptive routing. On the other hand, when a parallel program is executed on a multiprocessor the goal is to reduce the total execution time. We explain why improving the average message latency of a routing algorithm does not necessarily lead to a lower execution time for real applications. We support this observation by reporting simulation results for both adaptive and oblivious routing using communication derived from real applications. Specifically, we report the performance of various routing algorithms for directed acyclic graphs (DAGs) derived from the Cholesky factorization of sparse matrices. Our results show that there is little correlation between average message latency and the total execution time of a parallel program. Hence, average message latency does not seem to be a useful measure of the performance of a routing algorithm. This strongly suggests that current comparisons of routing algorithms do not provide a reliable indication of the performance improvements to be realized by executing programs on a multiprocessor with such a routing algorithm. We interpret these results and suggest several alternatives for further research.

This paper proposes a customized implementation of Mini-Mips processor; commencing from Register Transfer Level (RTL) down to GDSii Layout level. The implementation is customized for a string matching specific-application that is given problem for second Iranian National Digital Systems Design contest 2015 at Amirkabir University of Technology. We apply various techniques to make reasonable trade-off between area, delay, and power consumption ranging from loop unrolling in algorithm level to low power techniques such as clock gating in circuit level. Moreover, we utilize compiler optimization techniques to reduce the execution time. Our simulations show that execution time is reduced more than 63% on average. In overall, we come up with an optimized design in terms of chip area, power consumption and total execution time.

Grid system is used by many researchers’ and scholars all over the world to solve the complicated and complex problems in different sciences. Job ranking backfilling is the most used model by many researchers in grid system to improve the performance of job scheduling algorithm. The model aims on serving the smallest job in the queue. As a second improvement of job backfilling, researchers proposed job ranking backfilling that serve job based on ranking equation. This paper proposes an enhance job ranking algorithm based on using linear and logarithmic ranking equations. Both proposed ranking equations used curve estimation model to predict on the variables’ coefficients. By simulation and after different tests, the average results of job ranking backfilling with linear ranking equation outperform conventional job ranking backfilling with improvement equal 3.2% and 56.53% in total execution time and average waiting time, respectively. In addition, job ranking backfilling with logarithmic ranking equation shows average improvement equal 1.78% and 46.62% in total execution time and average waiting time, respectively. The results indicate that the proposed ranking equations would improve conventional job ranking backfilling in high and low demand grid system under different conditions

Quadratic Assignment Problem (QAP) is one of the most difficult combinatorial problems in the literature and has a diverse field of applications. This paper presents the results of experiments on the impact of parallelization of a sequential GA using island model. Both of the genetic algorithms are applied to the QAP. For the island model parallel GA, we systematically change the number of islands and investigate the effects of dividing the same global population into a number of subpopulations. The number of islands is gradually increased to observe the effects on solution quality and speedup in total execution time using different problem instances. The results clearly indicate that, while parallelized version outperforms sequential counterpart in both solution quality and total execution time, an increasing number of subpopulations also positively effects the results until a critical point where every subpopulation has a certain number of individuals to be able to evolve independently. Beyond that point, the performance of the algorithm begins to decrease.

NVIDIA GPUDirect is a family of technologies aimed at optimizing data movement among GPUs (P2P) or between GPUs and third-party devices (RDMA). GPUDirect Async, introduced in CUDA 8.0, is a new addition which allows direct synchronization between GPU and third party devices. For example, Async allows an NVIDIA GPU to directly trigger and poll for completion of communication operations queued to an InfiniBand Connect-IB network adapter, removing CPU involvement from the critical path in GPU accelerated applications. In this paper, we present the building blocks of GPUDirect Async and explain the supported usage models of this new technology. We also present a performance evaluation using a micro-benchmark and a synthetic stencil benchmark. Finally, we demonstrate the use of Async in a few multi-GPU MPI applications: HPGMG-FV (geometric multi-grid), achieving up to 25% improvement in total execution time; CoMD-CUDA (classical molecular dynamics), reducing communications times up to 30%; LULESH2-CUDA, achieving an average performance improvement of 13% in the total execution time.

Modulo scheduling algorithms based on optimal solvers have been proposed to investigate and tune the performance of modulo scheduling heuristics. While recent advances have broadened the scope for which the optimal approach is applicable, this approach increasingly suffers from large execution times. In this paper, we propose a more efficient formulation of the modulo scheduling space that significantly decreases the execution time of solvers based on integer linear programs. For example, the total execution time is reduced by a factor of 8.6 when 782 loops from the Perfect Club, SPEC, and Livermore Fortran Kernels are scheduled for minimum register requirements using the more efficient formulation instead of the traditional formulation. Experimental evidence further indicates that significantly larger loops can be scheduled under realistic machine constraints.

Energy-efficient scheduling of stochastic tasks is considered in this paper. The main characteristic of a stochastic task is that its execution time is a random variable whose actual value is not known in advance, but only its probability distribution. Our performance measures are the probability that the total execution time does not exceed a given bound and the probability that the total energy consumption does not exceed a given bound. Both probabilities need to be maximized. However, maximizations of the two performance measures are conflicting objectives. Our strategy is to fix one and maximize the other. Our investigation includes the following two aspects, with the purpose of maximizing the probability for the total execution time not to exceed a given bound, under the constraint that the probability for the total energy consumption not to exceed a given bound is at least certain value. First, we explore the technique of optimal processor speed setting for a given set of stochastic tasks on a processor with variable speed. It is found that the simple equal speed method (in which all tasks are executed with the same speed) yields high quality solutions. Second, we explore the technique of optimal stochastic task scheduling for a given set of stochastic tasks on a multiprocessor system, assuming that the equal speed method is used. We propose and evaluate the performance of several heuristic stochastic task scheduling algorithms. Our simulation studies identify the best methods among the proposed heuristic methods.

High-resolution global ocean general circulation models (OGCMs) play a key role in accurate ocean forecasting. However, the models of the operational forecasting systems are still not in high resolution due to the subsequent high demand for large computation, as well as the low parallel efficiency barrier. Good scalability is an important index of parallel efficiency and is still a challenge for OGCMs. We found that the communication cost in a barotropic solver, namely, the preconditioned conjugate gradient (PCG) method, is the key bottleneck for scalability due to the high frequency of the global reductions. In this work, we developed a new algorithm—a communication-avoiding Krylov subspace method with a PCG (CA-PCG)—to improve scalability and then applied it to the Nucleus for European Modelling of the Ocean (NEMO) as an example. For PCG, inner product operations with global communication were needed in every iteration, while for CA-PCG, inner product operations were only needed every eight iterations. Therefore, the global communication cost decreased from more than 94.5% of the total execution time with PCG to less than 63.4% with CA-PCG. As a result, the execution time of the barotropic modes decreased from more than 17,000 s with PCG to less than 6000 s with CA-PCG, and the total execution time decreased from more than 18,000 s with PCG to less than 6200 s with CA-PCG. Besides, the ratio of the speedup can also be increased from 3.7 to 4.6. In summary, the high process count scalability when using CA-PCG was effectively improved from that using the PCG method, providing a highly effective solution for accurate ocean simulation.

Cloud data centers, realizing that the amount of unused resources is significant, have started offering them as transient resources with unpredictable, irreversible revocation. The use of transient resources indicates many relevant issues still pose critical challenges, including security, strong and reliable connectivity and fault-tolerance approaches. To effectively use transient cloud servers to fulfill user requests, it is necessary to define an appropriate fault-tolerant mechanism and its respective parameters to avoid data loss if an unexpected failure occurs. We present an agent-based framework, namely BRA2Cloud, for integrating bag-of-tasks enabled systems using unreliable transient resources. To guarantee application execution and better use of idle resources, it is necessary to create an execution plan through fault tolerance definitions to increase reliability. To do this, BRA2Cloud agents combine features to predict failures in a multi-agent architecture that dynamically creates fault-tolerant multi-strategies, considering the current availability scenario and providing a resilient environment according to users’ application needs. Our approach was validated using real data retrieved between 2017 and 2019 from Amazon Spot Instances. Exhaustive experiments achieved high accuracy levels, reaching a 91% survival prediction success rate, which indicates the model is effective under realistic working conditions. We consider the results promising, decreasing up to 74.48% in total execution time when compared to other approaches in the literature. As the main requirements of our proposal, we have defined a series of features that BRA2Cloud should have in order to address the impact of these definitions on resiliency provision, application execution time reduction, and monetary cost reduction.