Automatic Skeleton-Driven Memory Affinity for Transactional Worklist Applications

doi: 10.1007/s10766-013-0253-xInternational audienceMemory affinity has become a key element to achieve scalable performance on multi-core platforms. Mechanisms such as thread scheduling, page allocation and cache prefetching are commonly employed to enhance memory affinity which keeps data close to the cores that access it. In particular, software transactional memory (STM) applications exhibit irregular memory access behavior that makes harder to determine which and when data will be needed by each core. Additionally, existing STM runtime systems are decoupled from issues such as thread and memory management. In this paper, we thus propose a skeleton-driven mechanism to improve memory affinity on STM applications that fit the worklist pattern employing a two-level approach. First, it addresses memory affinity in the DRAM level by automatic selecting page allocation policies. Then it employs data prefetching helper threads to improve affinity in the cache level. It relies on a skeleton framework to exploit the application pattern in order to provide automatic memory page allocation and cache prefetching. Our experimental results on the STAMP benchmark suite show that our proposed mechanism can achieve performance improvements of up to 46 %, with an average of 11 %, over a baseline version on two NUMA multi-core machines

Evaluation of Consumer Shopping Behavior in M-Commerce and E-Commerce

This paper intends to anlyze why e-commerce is more popular than mobile commerce among internet users, despite the growth of mobile market. Through survey research carried out among 130 users, it was possible to infer that people use mobile devices along the day and prefer using computers than mobile devices in all phases of their purchases. The possible reasons of that behavior is related to safety conditions of m-commerce and errors that occur on eletronic transactions. Mobile devices are used auxialiry tool to check available products on market and to follow the track of requests already done

Adaptive thread mapping strategies for transactional memory applications

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On the Efficacy, Efficiency and Emergent Behavior of Task Replication

Abstract: Large distributed systems challenge traditional schedulers, as it is often hard to determine a priori how long each task will take to complete on each resource, information that is input for such schedulers. Task replication has been applied in a variety of scenarios as a way to circumvent this problem. Task replication consists of dispatching multiple replicas of a task and using the result from the first replica to finish. Replication schedulers (i.e. schedulers that employ task replication) are able to achieve good performance even in the absence of information on tasks and resources. They are also of smaller complexity than traditional schedulers, making them better suitable for large distributed systems. On the other hand, replication schedulers waste cycles with the replicas that are not the first to finish. Moreover, this extra consumption of resources raises severe concerns about the system-wide performance of a distributed system with multiple, competing replication schedulers. This paper presents a comprehensive study of task replication, comparing replication schedulers against traditional information-based schedulers, and establishing their efficacy (the performance delivered to the application), efficiency (the amount of resources wasted), and emergent behavior (the system-wide behavior of a system with multiple replication schedulers). We also introduce a simple access control strategy that can be implemented locally by each resource and greatly improves overall performance of a system on which multiple replication schedulers compete for resources

Dynamic Thread Mapping Based on Machine Learning for Transactional Memory Applications

International audienceThread mapping is an appealing approach to efficiently exploit the potential of modern chip-multiprocessors. However, efficient thread mapping relies upon matching the behavior of an application with system characteristics. In particular, Software Transactional Memory (STM) introduces another dimension due to its runtime system support. In this work, we propose a dynamic thread mapping approach to automatically infer a suitable thread mapping strategy for transactional memory applications composed of multiple execution phases with potentially different transactional behavior in each phase. At runtime, it profiles the application at specific periods and consults a decision tree generated by a Machine Learning algorithm to decide if the current thread mapping strategy should be switched to a more adequate one. We implemented this approach in a state-of-the-art STM system, making it transparent to the user. Our results show that the proposed dynamic approach presents performance improvements up to 31% compared to the best static solution

AnthillSched: A Scheduling Strategy for Irregular and Iterative I/O-Intensive Parallel Jobs

Irregular and iterative I/O-intensive jobs need a different approach from parallel job schedulers. The focus in this case is not only the processing requirements anymore: memory, network and storage capacity must all be considered in making a scheduling decision. Job executions are irregular and data dependent, alternating between CPU-bound and I/O-bound phases. In this paper, we propose and implement a parallel job scheduling strategy for such jobs, called AnthillSched, based on a simple heuristic: we map the behavior of an parallel application with minimal resources as we vary its input parameters. From that mapping we infer the best scheduling for a certain set of input parameters given the available resources. To test and verify AnthillSched we used logs obtained from a real system executing data mining jobs. Our main contributions are the implementation of a parallel job scheduling strategy, called AnthillSched in a real system, and a performance analysis of AnthillSched, which allowed us to discard some other scheduling alternatives considered previously. 1