arXiv subjects

Florina M. Ciorba

Publications and source records attributed to Florina M. Ciorba.

At least 37 records · Page 2Linked to original sources

arXiv2020

Finding Neighbors in a Forest: A b-tree for Smoothed Particle Hydrodynamics Simulations

Finding the exact close neighbors of each fluid element in mesh-free computational hydrodynamical methods, such as the Smoothed Particle Hydrodynamics (SPH), often becomes a main bottleneck for scaling their performance beyond a few million fluid elements per computing node. Tree structures are particularly suitable for SPH simulation codes, which rely on finding the exact close neighbors of each fluid element (or SPH particle). In this work we present a novel tree structure, named \textit{$b$-tree}, which features an adaptive branching factor to reduce the depth of the neighbor search. Depending on the particle spatial distribution, finding neighbors using \tree has an asymptotic best case complexity of $O(n)$, as opposed to $O(n \log n)$ for other classical tree structures such as octrees and quadtrees. We also present the proposed tree structure as well as the algorithms to build it and to find the exact close neighbors of all particles. We assess the scalability of the proposed tree-based algorithms through an extensive set of performance experiments in a shared-memory system. Results show that b-tree is up to $12\times$ faster for building the tree and up to $1.6\times$ faster for finding the exact neighbors of all particles when compared to its octree form. Moreover, we apply b-tree to a SPH code and show its usefulness over the existing octree implementation, where b-tree is up to $5\times$ faster for finding the exact close neighbors compared to the legacy code.

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arXiv2020

A Smoothed Particle Hydrodynamics Mini-App for Exascale

The Smoothed Particles Hydrodynamics (SPH) is a particle-based, meshfree, Lagrangian method used to simulate multidimensional fluids with arbitrary geometries, most commonly employed in astrophysics, cosmology, and computational fluid-dynamics (CFD). It is expected that these computationally-demanding numerical simulations will significantly benefit from the up-and-coming Exascale computing infrastructures, that will perform 10 18 FLOP/s. In this work, we review the status of a novel SPH-EXA mini-app, which is the result of an interdisciplinary co-design project between the fields of astrophysics, fluid dynamics and computer science, whose goal is to enable SPH simulations to run on Exascale systems. The SPH-EXA mini-app merges the main characteristics of three state-of-the-art parent SPH codes (namely ChaNGa, SPH-flow, SPHYNX) with state-of-the-art (parallel) programming, optimization, and parallelization methods. The proposed SPH-EXA mini-app is a C++14 lightweight and flexible header-only code with no external software dependencies. Parallelism is expressed via multiple programming models, which can be chosen at compilation time with or without accelerator support, for a hybrid process+thread+accelerator configuration. Strong and weak-scaling experiments on a production supercomputer show that the SPH-EXA mini-app can be efficiently executed with up 267 million particles and up to 65 billion particles in total on 2,048 hybrid CPU-GPU nodes.

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arXiv2019

SimAS: A Simulation-assisted Approach for the Scheduling Algorithm Selection under Perturbations

Many scientific applications consist of large and computationally-intensive loops. Dynamic loop self-scheduling (DLS) techniques are used to parallelize and to balance the load during the execution of such applications. Load imbalance arises from variations in the loop iteration (or tasks) execution times, caused by problem, algorithmic, or systemic characteristics. The variations in systemic characteristics are referred to as perturbations, and can be caused by other applications or processes that share the same resources, or a temporary system fault or malfunction. Therefore, the selection of the most efficient DLS technique is critical to achieve the best application performance. The following question motivates this work: Given an application, an HPC system, and their characteristics and interplay, which DLS technique will achieve improved performance under unpredictable perturbations? Existing studies focus on variations in the delivered computational speed only as the source of perturbations in the system. However, perturbations in available network bandwidth or latency are inevitable on production HPC systems. A Simulator-assisted scheduling (SimAS) is introduced as a new control-theoretic-inspired approach to dynamically select DLS techniques that improve the performance of applications executing on heterogeneous HPC systems under perturbations. The present work examines the performance of seven applications on a heterogeneous system under all the above system perturbations. SimAS is evaluated as a proof of concept using native and simulative experiments. The performance results confirm the original hypothesis that no single DLS technique can deliver the absolute best performance in all scenarios, whereas the SimAS-based DLS selection resulted in improved application performance in most experiments.

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arXiv2019

Two-level Dynamic Load Balancing for High Performance Scientific Applications

Scientific applications are often complex, irregular, and computationally-intensive. To accommodate the ever-increasing computational demands of scientific applications, high-performance computing (HPC) systems have become larger and more complex, offering parallelism at multiple levels (e.g., nodes, cores per node, threads per core). Scientific applications need to exploit all the available multilevel hardware parallelism to harness the available computational power. The performance of applications executing on such HPC systems may adversely be affected by load imbalance at multiple levels, caused by problem, algorithmic, and systemic characteristics. Nevertheless, most existing load balancing methods do not simultaneously address load imbalance at multiple levels. This work investigates the impact of load imbalance on the performance of three scientific applications at the thread and process levels. We jointly apply and evaluate selected dynamic loop self-scheduling (DLS) techniques to both levels. Specifically, we employ the extended LaPeSD OpenMP runtime library at the thread level and extend the DLS4LB MPI-based dynamic load balancing library at the process level. This approach is generic and applicable to any multiprocess-multithreaded computationally-intensive application (programmed using MPI and OpenMP). We conduct an exhaustive set of experiments to assess and compare six DLS techniques at the thread level and eleven at the process level. The results show that improved application performance, by up to 21%, can only be achieved by jointly addressing load imbalance at the two levels. We offer insights into the performance of the selected DLS techniques and discuss the interplay of load balancing at the thread level and process level.

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arXiv2019

An Approach for Realistically Simulating the Performance of Scientific Applications on High Performance Computing Systems

Scientific applications often contain large, computationally-intensive, and irregular parallel loops or tasks that exhibit stochastic characteristics. Applications may suffer from load imbalance during their execution on high-performance computing (HPC) systems due to such characteristics. Dynamic loop self-scheduling (DLS) techniques are instrumental in improving the performance of scientific applications on HPC systems via load balancing. Selecting a DLS technique that results in the best performance for different problems and system sizes requires a large number of exploratory experiments. A theoretical model that can be used to predict the scheduling technique that yields the best performance for a given problem and system has not yet been identified. Therefore, simulation is the most appropriate approach for conducting such exploratory experiments with reasonable costs. This work devises an approach to realistically simulate computationally-intensive scientific applications that employ DLS and execute on HPC systems. Several approaches to represent the application tasks (or loop iterations) are compared to establish their influence on the simulative application performance. A novel simulation strategy is introduced, which transforms a native application code into a simulative code. The native and simulative performance of two computationally-intensive scientific applications are compared to evaluate the realism of the proposed simulation approach. The comparison of the performance characteristics extracted from the native and simulative performance shows that the proposed simulation approach fully captured most of the performance characteristics of interest. This work shows and establishes the importance of simulations that realistically predict the performance of DLS techniques for different applications and system configurations.

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arXiv2019

rDLB: A Novel Approach for Robust Dynamic Load Balancing of Scientific Applications with Parallel Independent Tasks

Scientific applications often contain large and computationally intensive parallel loops. Dynamic loop self scheduling (DLS) is used to achieve a balanced load execution of such applications on high performance computing (HPC) systems. Large HPC systems are vulnerable to processors or node failures and perturbations in the availability of resources. Most self-scheduling approaches do not consider fault-tolerant scheduling or depend on failure or perturbation detection and react by rescheduling failed tasks. In this work, a robust dynamic load balancing (rDLB) approach is proposed for the robust self scheduling of independent tasks. The proposed approach is proactive and does not depend on failure or perturbation detection. The theoretical analysis of the proposed approach shows that it is linearly scalable and its cost decrease quadratically by increasing the system size. rDLB is integrated into an MPI DLS library to evaluate its performance experimentally with two computationally intensive scientific applications. Results show that rDLB enables the tolerance of up to (P minus one) processor failures, where P is the number of processors executing an application. In the presence of perturbations, rDLB boosted the robustness of DLS techniques up to 30 times and decreased application execution time up to 7 times compared to their counterparts without rDLB.

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arXiv2019

Algorithm-Based Fault Tolerance for Parallel Stencil Computations

The increase in HPC systems size and complexity, together with increasing on-chip transistor density, power limitations, and number of components, render modern HPC systems subject to soft errors. Silent data corruptions (SDCs) are typically caused by such soft errors in the form of bit-flips in the memory subsystem and hinder the correctness of scientific applications. This work addresses the problem of protecting a class of iterative computational kernels, called stencils, against SDCs when executing on parallel HPC systems. Existing SDC detection and correction methods are in general either inaccurate, inefficient, or targeting specific application classes that do not include stencils. This work proposes a novel algorithm-based fault tolerance (ABFT) method to protect scientific applications that contain arbitrary stencil computations against SDCs. The ABFT method can be applied both online and offline to accurately detect and correct SDCs in 2D and 3D parallel stencil computations. We present a formal model for the proposed method including theorems and proofs for the computation of the associated checksums as well as error detection and correction. We experimentally evaluate the use of the proposed ABFT method on a real 3D stencil-based application (HotSpot3D) via a fault-injection, detection, and correction campaign. Results show that the proposed ABFT method achieves less than 8% overhead compared to the performance of the unprotected stencil application. Moreover, it accurately detects and corrects SDCs. While the offline ABFT version corrects errors more accurately, it may incur a small additional overhead than its online counterpart.

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arXiv2019

Toward a Standard Interface for User-Defined Scheduling in OpenMP

Parallel loops are an important part of OpenMP programs. Efficient scheduling of parallel loops can improve performance of the programs. The current OpenMP specification only offers three options for loop scheduling, which are insufficient in certain instances. Given the large number of other possible scheduling strategies, it is infeasible to standardize each one. A more viable approach is to extend the OpenMP standard to allow for users to define loop scheduling strategies. The approach will enable standard-compliant application-specific scheduling. This work analyzes the principal components required by user-defined scheduling and proposes two competing interfaces as candidates for the OpenMP standard. We conceptually compare the two proposed interfaces with respect to the three host languages of OpenMP, i.e., C, C++, and Fortran. These interfaces serve the OpenMP community as a basis for discussion and prototype implementation for user-defined scheduling.

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arXiv2019

Anomaly Detection in High Performance Computers: A Vicinity Perspective

In response to the demand for higher computational power, the number of computing nodes in high performance computers (HPC) increases rapidly. Exascale HPC systems are expected to arrive by 2020. With drastic increase in the number of HPC system components, it is expected to observe a sudden increase in the number of failures which, consequently, poses a threat to the continuous operation of the HPC systems. Detecting failures as early as possible and, ideally, predicting them, is a necessary step to avoid interruptions in HPC systems operation. Anomaly detection is a well-known general purpose approach for failure detection, in computing systems. The majority of existing methods are designed for specific architectures, require adjustments on the computing systems hardware and software, need excessive information, or pose a threat to users' and systems' privacy. This work proposes a node failure detection mechanism based on a vicinity-based statistical anomaly detection approach using passively collected and anonymized system log entries. Application of the proposed approach on system logs collected over 8 months indicates an anomaly detection precision between 62% to 81%.

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arXiv2019

SPH-EXA: Enhancing the Scalability of SPH codes Via an Exascale-Ready SPH Mini-App

Numerical simulations of fluids in astrophysics and computational fluid dynamics (CFD) are among the most computationally-demanding calculations, in terms of sustained floating-point operations per second, or FLOP/s. It is expected that these numerical simulations will significantly benefit from the future Exascale computing infrastructures, that will perform 10^18 FLOP/s. The performance of the SPH codes is, in general, adversely impacted by several factors, such as multiple time-stepping, long-range interactions, and/or boundary conditions. In this work an extensive study of three SPH implementations SPHYNX, ChaNGa, and XXX is performed, to gain insights and to expose any limitations and characteristics of the codes. These codes are the starting point of an interdisciplinary co-design project, SPH-EXA, for the development of an Exascale-ready SPH mini-app. We implemented a rotating square patch as a joint test simulation for the three SPH codes and analyzed their performance on a modern HPC system, Piz Daint. The performance profiling and scalability analysis conducted on the three parent codes allowed to expose their performance issues, such as load imbalance, both in MPI and OpenMP. Two-level load balancing has been successfully applied to SPHYNX to overcome its load imbalance. The performance analysis shapes and drives the design of the SPH-EXA mini-app towards the use of efficient parallelization methods, fault-tolerance mechanisms, and load balancing approaches.

physics.comp-ph↗

arXiv2019

Detection of Silent Data Corruptions in Smoothed Particle Hydrodynamics Simulations

Silent data corruptions (SDCs) hinder the correctness of long-running scientific applications on large scale computing systems. Selective particle replication (SPR) is proposed herein as the first particle-based replication method for detecting SDCs in Smoothed particle hydrodynamics (SPH) simulations. SPH is a mesh-free Lagrangian method commonly used to perform hydrodynamical simulations in astrophysics and computational fluid dynamics. SPH performs interpolation of physical properties over neighboring discretization points (called SPH particles) that dynamically adapt their distribution to the mass density field of the fluid. When a fault (e.g., a bit-flip) strikes the computation or the data associated with a particle, the resulting error is silently propagated to all nearest neighbors through such interpolation steps. SPR replicates the computation and data of a few carefully selected SPH particles. SDCs are detected when the data of a particle differs, due to corruption, from its replicated counterpart. SPR is able to detect many DRAM SDCs as they propagate by ensuring that all particles have at least one neighbor that is replicated. The detection capabilities of SPR were assessed through a set of error-injection and detection experiments and the overhead of SPR was evaluated via a set of strong-scaling experiments conducted on an HPC system. The results show that SPR achieves detection rates of 91-99.9%, no false-positives, at an overhead of 1-10%.

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arXiv2019

Hierarchical Dynamic Loop Self-Scheduling on Distributed-Memory Systems Using an MPI+MPI Approach

Computationally-intensive loops are the primary source of parallelism in scientific applications. Such loops are often irregular and a balanced execution of their loop iterations is critical for achieving high performance. However, several factors may lead to an imbalanced load execution, such as problem characteristics, algorithmic, and systemic variations. Dynamic loop self-scheduling (DLS) techniques are devised to mitigate these factors, and consequently, improve application performance. On distributed-memory systems, DLS techniques can be implemented using a hierarchical master-worker execution model and are, therefore, called hierarchical DLS techniques. These techniques self-schedule loop iterations at two levels of hardware parallelism: across and within compute nodes. Hybrid programming approaches that combine the message passing interface (MPI) with open multi-processing (OpenMP) dominate the implementation of hierarchical DLS techniques. The MPI-3 standard includes the feature of sharing memory regions among MPI processes. This feature introduced the MPI+MPI approach that simplifies the implementation of parallel scientific applications. The present work designs and implements hierarchical DLS techniques by exploiting the MPI+MPI approach. Four well-known DLS techniques are considered in the evaluation proposed herein. The results indicate certain performance advantages of the proposed approach compared to the hybrid MPI+OpenMP approach.

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arXiv2019

Turning Privacy Constraints into Syslog Analysis Advantage

The mean time between failures (MTBF) of HPC systems is rapidly reducing, and that current failure recovery mechanisms e.g., checkpoint-restart, will no longer be able to recover the systems from failures. Early failure detection is a new class of failure recovery methods that can be beneficial for HPC systems with short MTBF. System logs (syslogs) are invaluable source of information which give us a deep insight about system behavior, and make the early failure detection possible. Beside normal information, syslogs contain sensitive data which might endanger users' privacy. Even though analyzing various syslogs is necessary for creating a general failure detection/prediction method, privacy concerns discourage system administrators to publish syslogs. Herein, we ensure user privacy via de-identifying syslogs, and then turning the applied constraint for addressing users' privacy into an advantage for system behavior analysis. Results indicate significant reduction in required storage space and 3 times shorter processing time.

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arXiv2018

Dynamic Loop Scheduling Using MPI Passive-Target Remote Memory Access

Scientific applications often contain large computationally-intensive parallel loops. Loop scheduling techniques aim to achieve load balanced executions of such applications. For distributed-memory systems, existing dynamic loop scheduling (DLS) libraries are typically MPI-based, and employ a master-worker execution model to assign variably-sized chunks of loop iterations. The master-worker execution model may adversely impact performance due to the master-level contention. This work proposes a distributed chunk-calculation approach that does not require the master-worker execution scheme. Moreover, it considers the novel features in the latest MPI standards, such as passive-target remote memory access, shared-memory window creation, and atomic read-modify-write operations. To evaluate the proposed approach, five well-known DLS techniques, two applications, and two heterogeneous hardware setups have been considered. The DLS techniques implemented using the proposed approach outperformed their counterparts implemented using the traditional master-worker execution model.

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arXiv2018

Exploring the Relation Between Two Levels of Scheduling Using a Novel Simulation Approach

Modern high performance computing (HPC) systems exhibit a rapid growth in size, both "horizontally" in the number of nodes, as well as "vertically" in the number of cores per node. As such, they offer additional levels of hardware parallelism. Each such level requires and employs algorithms for appropriately scheduling the computational work at the respective level. The present work explores the relation between two scheduling levels: batch and application. Understanding this relation is important for improving the performance of scientific applications, that are scheduled and executed in batches on HPC systems. The relation between batch and application level scheduling is understudied in the literature. Understanding the relation and interaction between these two scheduling levels requires their simultaneous analysis during operation. In this work, such an analysis is performed via simultaneous simulation of batch and application level scheduling for a number of scenarios. A generic simulation approach is presented that bridges two existing simulators from the two scheduling levels. A novel two-level simulator that implements the proposed approach is introduced. The two-level simulator is used to simulate all combinations of three batch scheduling and four application scheduling algorithms from the literature. These combinations are considered for allocating resources and executing the parallel jobs of two batches from two production HPC systems. The results of the scheduling experiments reveal the strong relation between the two scheduling levels and their mutual influence. Complementing the simulations, the two-level simulator produces standard parallel execution traces, which can visually be examined and which illustrate the execution of different jobs and, for each job, the execution of its tasks at node and core levels, respectively.

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arXiv2018

Efficient Generation of Parallel Spin-images Using Dynamic Loop Scheduling

High performance computing (HPC) systems underwent a significant increase in their processing capabilities. Modern HPC systems combine large numbers of homogeneous and heterogeneous computing resources. Scalability is, therefore, an essential aspect of scientific applications to efficiently exploit the massive parallelism of modern HPC systems. This work introduces an efficient version of the parallel spin-image algorithm (PSIA), called EPSIA. The PSIA is a parallel version of the spin-image algorithm (SIA). The (P)SIA is used in various domains, such as 3D object recognition, categorization, and 3D face recognition. EPSIA refers to the extended version of the PSIA that integrates various well-known dynamic loop scheduling (DLS) techniques. The present work: (1) Proposes EPSIA, a novel flexible version of PSIA; (2) Showcases the benefits of applying DLS techniques for optimizing the performance of the PSIA; (3) Assesses the performance of the proposed EPSIA by conducting several scalability experiments. The performance results are promising and show that using well-known DLS techniques, the performance of the EPSIA outperforms the performance of the PSIA by a factor of 1.2 and 2 for homogeneous and heterogeneous computing resources, respectively.

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arXiv2018

Towards a Mini-App for Smoothed Particle Hydrodynamics at Exascale

The smoothed particle hydrodynamics (SPH) technique is a purely Lagrangian method, used in numerical simulations of fluids in astrophysics and computational fluid dynamics, among many other fields. SPH simulations with detailed physics represent computationally-demanding calculations. The parallelization of SPH codes is not trivial due to the absence of a structured grid. Additionally, the performance of the SPH codes can be, in general, adversely impacted by several factors, such as multiple time-stepping, long-range interactions, and/or boundary conditions. This work presents insights into the current performance and functionalities of three SPH codes: SPHYNX, ChaNGa, and SPH-flow. These codes are the starting point of an interdisciplinary co-design project, SPH-EXA, for the development of an Exascale-ready SPH mini-app. To gain such insights, a rotating square patch test was implemented as a common test simulation for the three SPH codes and analyzed on two modern HPC systems. Furthermore, to stress the differences with the codes stemming from the astrophysics community (SPHYNX and ChaNGa), an additional test case, the Evrard collapse, has also been carried out. This work extrapolates the common basic SPH features in the three codes for the purpose of consolidating them into a pure-SPH, Exascale-ready, optimized, mini-app. Moreover, the outcome of this serves as direct feedback to the parent codes, to improve their performance and overall scalability.

physics.comp-ph↗

arXiv2018

OpenMP Loop Scheduling Revisited: Making a Case for More Schedules

In light of continued advances in loop scheduling, this work revisits the OpenMP loop scheduling by outlining the current state of the art in loop scheduling and presenting evidence that the existing OpenMP schedules are insufficient for all combinations of applications, systems, and their characteristics. A review of the state of the art shows that due to the specifics of the parallel applications, the variety of computing platforms, and the numerous performance degradation factors, no single loop scheduling technique can be a 'one-fits-all' solution to effectively optimize the performance of all parallel applications in all situations. The impact of irregularity in computational workloads and hardware systems, including operating system noise, on the performance of parallel applications, results in performance loss and has often been neglected in loop scheduling research, in particular, the context of OpenMP schedules. Existing dynamic loop self-scheduling techniques, such as trapezoid self-scheduling, factoring, and weighted factoring, offer an unexplored potential to alleviate this degradation in OpenMP due to the fact that they explicitly target the minimization of load imbalance and scheduling overhead. Through theoretical and experimental evaluation, this work shows that these loop self-scheduling methods provide a benefit in the context of OpenMP. In conclusion, OpenMP must include more schedules to offer a broader performance coverage of applications executing on an increasing variety of heterogeneous shared memory computing platforms.

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