Towards Faster Reasoners by Using Transparent Huge Pages

Fichte, Johannes Klaus; Manthey, Norbert; Stecklina, Julian; Schidler, André

doi:10.1007/978-3-030-58475-7_18

Cited by 9 publications

(7 citation statements)

References 31 publications

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“…We disabled the features that may affect the performance stability, such as Intel Turbo Boost [54], dynamic voltage and frequency scaling [55], C-state/P-state controls [56], and transparent huge page support [57]. The use of huge pages is usually effective in improving the performance of applications due to increased TLB reaches and reduced TLB misses [57], [58]. In HPC systems, however, it is a well-known source of degrading the performance stability; a larger page can produce more performance imbalance due to higher probability of containing data with different memory access characteristics [59]- [61].…”

Section: Discussionmentioning

confidence: 99%

A Performance-Stable NUMA Management Scheme for Linux-Based HPC Systems

et al. 2021

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Linux is becoming the de-facto standard operating system for today's high-performance computing (HPC) systems because it can satisfy the demands of many HPC systems for rich operating system (OS) features. However, owing to features intended for the general-purpose OS, Linux has many OS noise sources such as page faults or thread migrations that can result in the unstable performance of HPC application. Furthermore, in the case of the non-uniform memory access (NUMA) architecture, which has different memory access latencies to local and remote memory nodes, the performance stability of the application can be more exacerbated by the OS noise. In this paper, we address the OS noise caused by Linux in the NUMA architecture and propose a novel performance-stable NUMA management scheme called Stable-NUMA. Stable-NUMA comprises three techniques for improving performance stability: two-level thread clustering, state-based page placement, and selective page profiling. Our proposed Stable-NUMA scheme significantly alleviates OS noise and enhances the local memory access ratio of the NUMA system as compared to Linux. We implemented Stable-NUMA in Linux and experimented with various HPC workloads. The evaluation results demonstrated that Stable-NUMA outperforms Linux with and without its NUMA-aware feature by up to 25% in terms of average performance and 73% in terms of performance stability.

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Section: Discussionmentioning

confidence: 99%

A Performance-Stable NUMA Management Scheme for Linux-Based HPC Systems

et al. 2021

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“…Results are gathered on Linux RHEL 7 powered on kernel 3.10.0-1127.19.1.el7 with hyperthreading disabled. Transparent huge pages are set to system default (Fichte et al, 2020). We follow standard guidelines for empirical evaluations (van der Kouwe et al, 2018;Fichte et al, 2021b) and measure runtime using perf and enforce limits using runsolver (Roussel, 2011).…”

Section: Empirical Evaluationmentioning

confidence: 99%

Exploiting Database Management Systems and Treewidth for Counting

Fichte¹,

Hecher²,

Thier³

et al. 2021

Theory and Practice of Logic Programming

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Bounded treewidth is one of the most cited combinatorial invariants in the literature. It was also applied for solving several counting problems efficiently. A canonical counting problem is #Sat, which asks to count the satisfying assignments of a Boolean formula. Recent work shows that benchmarking instances for #Sat often have reasonably small treewidth. This paper deals with counting problems for instances of small treewidth. We introduce a general framework to solve counting questions based on state-of-the-art database management systems (DBMSs). Our framework takes explicitly advantage of small treewidth by solving instances using dynamic programming (DP) on tree decompositions (TD). Therefore, we implement the concept of DP into a DBMS (PostgreSQL), since DP algorithms are already often given in terms of table manipulations in theory. This allows for elegant specifications of DP algorithms and the use of SQL to manipulate records and tables, which gives us a natural approach to bring DP algorithms into practice. To the best of our knowledge, we present the first approach to employ a DBMS for algorithms on TDs. A key advantage of our approach is that DBMSs naturally allow for dealing with huge tables with a limited amount of main memory (RAM).

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“…We forced performance governors to 2.2 GHz clock speed [71], disabled hyper threading, and enforced the process that handles the solver invocation to run on cores 0,1,14,15 and enforced solvers to run on cores 2-6, 7-11, 14-18, and 19-23. We explicitly disabled transparent huge pages [34].…”

Section: Submission Requirements and Limitsmentioning

confidence: 99%

The Model Counting Competition 2020

Fichte¹,

Hecher²,

Hamiti³

2020

Preprint

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Many computational problems in modern society account to probabilistic reasoning, statistics, and combinatorics. A variety of these real-world questions can be solved by representing the question in (Boolean) formulas and associating the number of models of the formula directly with the answer to the question. Since there has been an increasing interest in practical problem solving for model counting over the last years, the Model Counting (MC) Competition was conceived in fall 2019. The competition aims to foster applications, identify new challenging benchmarks, and to promote new solvers and improve established solvers for the model counting problem and versions thereof. We hope that the results can be a good indicator of the current feasibility of model counting and spark many new applications. In this paper, we report on details of the Model Counting Competition 2020, about carrying out the competition, and the results. The competition encompassed three versions of the model counting problem, which we evaluated in separate tracks. The first track featured the model counting problem (MC), which asks for the number of models of a given Boolean formula. On the second track, we challenged developers to submit programs that solve the weighted model counting problem (WMC). The last track was dedicated to projected model counting (PMC). In total, we received a surprising number of 9 solvers in 34 versions from 8 groups.

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Towards Faster Reasoners by Using Transparent Huge Pages

Cited by 9 publications

References 31 publications

A Performance-Stable NUMA Management Scheme for Linux-Based HPC Systems

A Performance-Stable NUMA Management Scheme for Linux-Based HPC Systems

Exploiting Database Management Systems and Treewidth for Counting

The Model Counting Competition 2020

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