BarrierPoint: sampled simulation of multi-threaded applications

Sampling is a well-known technique to speed up architectural simulation of long-running workloads while maintaining accurate performance predictions. A number of sampling techniques have recently been developed that extend well- known single-threaded techniques to allow sampled simulation of multi-threaded applications. Unfortunately, prior work is limited to non-synchronizing applications (e.g., server throughput workloads); requires the functional simulation of the entire application using a detailed cache hierarchy which limits the overall simulation speedup potential; leads to different units of work across different processor architectures which complicates performance analysis; or, requires massive machine resources to achieve reasonable simulation speedups. In this work, we propose BarrierPoint, a sampling methodology to accelerate simulation by leveraging globally synchronizing barriers in multi-threaded applications. BarrierPoint collects microarchitecture-independent code and data signatures to determine the most representative inter-barrier regions, called barrierpoints. BarrierPoint estimates total application execution time (and other performance metrics of interest) through detailed simulation of these barrierpoints only, leading to substantial simulation speedups. Barrierpoints can be simulated in parallel, use fewer simulation resources, and define fixed units of work to be used in performance comparisons across processor architectures. Our evaluation of BarrierPoint using NPB and Parsec benchmarks reports average simulation speedups of 24.7x (and up to 866.6x) with an average simulation error of 0.9% and 2.9% at most. On average, BarrierPoint reduces the number of simulation machine resources needed by 78x

RPPM : Rapid Performance Prediction of Multithreaded workloads on multicore processors

Analytical performance modeling is a useful complement to detailed cycle-level simulation to quickly explore the design space in an early design stage. Mechanistic analytical modeling is particularly interesting as it provides deep insight and does not require expensive offline profiling as empirical modeling. Previous work in mechanistic analytical modeling, unfortunately, is limited to single-threaded applications running on single-core processors. This work proposes RPPM, a mechanistic analytical performance model for multi-threaded applications on multicore hardware. RPPM collects microarchitecture-independent characteristics of a multi-threaded workload to predict performance on a previously unseen multicore architecture. The profile needs to be collected only once to predict a range of processor architectures. We evaluate RPPM's accuracy against simulation and report a performance prediction error of 11.2% on average (23% max). We demonstrate RPPM's usefulness for conducting design space exploration experiments as well as for analyzing parallel application performance

Measuring and Managing Answer Quality for Online Data-Intensive Services

Online data-intensive services parallelize query execution across distributed software components. Interactive response time is a priority, so online query executions return answers without waiting for slow running components to finish. However, data from these slow components could lead to better answers. We propose Ubora, an approach to measure the effect of slow running components on the quality of answers. Ubora randomly samples online queries and executes them twice. The first execution elides data from slow components and provides fast online answers; the second execution waits for all components to complete. Ubora uses memoization to speed up mature executions by replaying network messages exchanged between components. Our systems-level implementation works for a wide range of platforms, including Hadoop/Yarn, Apache Lucene, the EasyRec Recommendation Engine, and the OpenEphyra question answering system. Ubora computes answer quality much faster than competing approaches that do not use memoization. With Ubora, we show that answer quality can and should be used to guide online admission control. Our adaptive controller processed 37% more queries than a competing controller guided by the rate of timeouts.Comment: Technical Repor

Improving the Accuracy and Efficiency of Time-Independent Trace Replay

Simulation is a popular approach to obtain objective performance indicators on platforms that are not at one's disposal. It may help the dimensioning of compute clusters in large computing centers. In a previous work, we proposed a framework for the off-line simulation of MPI applications. Its main originality with regard to the literature is to rely on time-independent execution traces. This allows us to completely decouple the acquisition process from the actual replay of the traces in a simulation context. Then we are able to acquire traces for large application instances without being limited to an execution on a single compute cluster. Finally our framework is built on top of a scalable, fast, and validated simulation kernel. In this paper, we detail the performance issues that we encountered with the first implementation of our trace replay framework. We propose several modifications to address these issues and analyze their impact. Results shows a clear improvement on the accuracy and efficiency with regard to the initial implementation.La simulation est une approche populaire pour obtenir des indicateurs de performance objectifs sur des plates-formes qui ne sont pas nécessairement accessibles. Elle peut par exemple aider au dimensionnement d'infrastructures dans de grands centres de calcul. Dans un article précédent, nous avons proposé un environnement pour la simulation hors-ligne d'applications MPI. La principale originalité de cet environnement par rapport à la littérature est de ne reposer que sur des traces indépendantes du temps. Cela nous permet de découpler totalement l'acquisition des traces de leur rejeu simulé effectif. Nous sommes ainsi capables d'obtenir des traces pour de très grandes instances d'applications sans être limités à une exécution au sein d'une seule grappe de machines. Enfin, cet environnement est fondé sur un noyau de simulation extensible, rapide et validé. Dans cet article nous détaillons les problèmes de performance rencontrés par la première implantation de notre environnement de rejeu de traces. Nous proposons plusieurs modifications pour résoudre ces problèmes et analysons leur impact. Les résultats obtenus montrent une amélioration notable à la fois en termes de précision et d'efficacité par rapport à l'implantation initiale

Predicting Scheduling Failures in the Cloud

Cloud Computing has emerged as a key technology to deliver and manage computing, platform, and software services over the Internet. Task scheduling algorithms play an important role in the efficiency of cloud computing services as they aim to reduce the turnaround time of tasks and improve resource utilization. Several task scheduling algorithms have been proposed in the literature for cloud computing systems, the majority relying on the computational complexity of tasks and the distribution of resources. However, several tasks scheduled following these algorithms still fail because of unforeseen changes in the cloud environments. In this paper, using tasks execution and resource utilization data extracted from the execution traces of real world applications at Google, we explore the possibility of predicting the scheduling outcome of a task using statistical models. If we can successfully predict tasks failures, we may be able to reduce the execution time of jobs by rescheduling failed tasks earlier (i.e., before their actual failing time). Our results show that statistical models can predict task failures with a precision up to 97.4%, and a recall up to 96.2%. We simulate the potential benefits of such predictions using the tool kit GloudSim and found that they can improve the number of finished tasks by up to 40%. We also perform a case study using the Hadoop framework of Amazon Elastic MapReduce (EMR) and the jobs of a gene expression correlations analysis study from breast cancer research. We find that when extending the scheduler of Hadoop with our predictive models, the percentage of failed jobs can be reduced by up to 45%, with an overhead of less than 5 minutes

Efficient Online Scheduling in Distributed Stream Data Processing Systems

General-purpose Distributed Stream Data Processing Systems (DSDPSs) have attracted extensive attention from industry and academia in recent years. They are capable of processing unbounded big streams of continuous data in a distributed and real (or near-real) time manner. A fundamental problem in a DSDPS is the scheduling problem, i.e., assigning threads (carrying workload) to workers/machines with the objective of minimizing average end-to-end tuple processing time (or simply tuple processing time). A widely-used solution is to distribute workload over machines in the cluster in a round-robin manner, which is obviously not efficient due to the lack of consideration for communication delay among processes/machines. A scheduling solution makes a significant impact on the average tuple processing time. However, their relationship is very subtle and complicated. It does not even seem possible to have a mathematical programming formulation for the scheduling problem if its objective is to directly minimize the average tuple processing time. In this dissertation, we first propose a model-based approach that accurately models the correlation between a scheduling solution and its objective value (i.e. average tuple processing time) for a given scheduling solution according to the topology of the application graph and runtime statistics. A predictive scheduling algorithm is then presented, which as- signs tasks (threads) to machines under the guidance of the proposed model. This approach achieves an average of 24.9% improvement over Storm’s default scheduler. However, the model-based approach still has its limitations: the model may not be able to fully capture the features of a DSDPS; prediction may not be accurate enough; and a large amount of high-dimensional data may lead to high overhead. To address the limitations, we develop a model-free approach that can learn to control a DSDPS from its experience rather than adopting accurate and mathematically solvable system models, just as a human learns a skill (such as cooking, driving, swimming, etc.). Recent breakthrough of Deep Reinforcement Learning (DRL) provides a promising approach for enabling effective model-free control. The proposed DRL-based model-free approach minimizes the average end-to-end tuple processing time by jointly learning the system environment via collecting very limited runtime statistics and making decisions under the guidance of powerful Deep Neural Networks (DNNs). This approach achieves great performance improvement over the current practice and the state-of-the-art model-based approach. Moreover, there is still room for improvement for the above model-free approach: For the above model-free approach and most existing methods, a user specifies the number of threads for an application in advance without knowing much about runtime needs, which, however, remains unchanged during runtime. This could severely affect the performance of a DSDPS. Therefore, we further develop another model-free approach using DRL, EXTRA, which enables the dynamic use of a variable number of threads at runtime. It has been shown by extensive experimental results, by adding this new feature, EXTRA can achieve further performance improvement and greater flexibility on scheduling