A Tuned and Scalable Fast Multipole Method as a Preeminent Algorithm for Exascale Systems

Among the algorithms that are likely to play a major role in future exascale computing, the fast multipole method (FMM) appears as a rising star. Our previous recent work showed scaling of an FMM on GPU clusters, with problem sizes in the order of billions of unknowns. That work led to an extremely parallel FMM, scaling to thousands of GPUs or tens of thousands of CPUs. This paper reports on a a campaign of performance tuning and scalability studies using multi-core CPUs, on the Kraken supercomputer. All kernels in the FMM were parallelized using OpenMP, and a test using 10^7 particles randomly distributed in a cube showed 78% efficiency on 8 threads. Tuning of the particle-to-particle kernel using SIMD instructions resulted in 4x speed-up of the overall algorithm on single-core tests with 10^3 - 10^7 particles. Parallel scalability was studied in both strong and weak scaling. The strong scaling test used 10^8 particles and resulted in 93% parallel efficiency on 2048 processes for the non-SIMD code and 54% for the SIMD-optimized code (which was still 2x faster). The weak scaling test used 10^6 particles per process, and resulted in 72% efficiency on 32,768 processes, with the largest calculation taking about 40 seconds to evaluate more than 32 billion unknowns. This work builds up evidence for our view that FMM is poised to play a leading role in exascale computing, and we end the paper with a discussion of the features that make it a particularly favorable algorithm for the emerging heterogeneous and massively parallel architectural landscape

Scientific Grand Challenges: Crosscutting Technologies for Computing at the Exascale - February 2-4, 2010, Washington, D.C.

The goal of the "Scientific Grand Challenges - Crosscutting Technologies for Computing at the Exascale" workshop in February 2010, jointly sponsored by the U.S. Department of Energy’s Office of Advanced Scientific Computing Research and the National Nuclear Security Administration, was to identify the elements of a research and development agenda that will address these challenges and create a comprehensive exascale computing environment. This exascale computing environment will enable the science applications identified in the eight previously held Scientific Grand Challenges Workshop Series

Parallel programming systems for scalable scientific computing

High-performance computing (HPC) systems are more powerful than ever before. However, this rise in performance brings with it greater complexity, presenting significant challenges for researchers who wish to use these systems for their scientific work. This dissertation explores the development of scalable programming solutions for scientific computing. These solutions aim to be effective across a diverse range of computing platforms, from personal desktops to advanced supercomputers.To better understand HPC systems, this dissertation begins with a literature review on exascale supercomputers, massive systems capable of performing 10¹⁸ floating-point operations per second. This review combines both manual and data-driven analyses, revealing that while traditional challenges of exascale computing have largely been addressed, issues like software complexity and data volume remain. Additionally, the dissertation introduces the open-source software tool (called LitStudy) developed for this research.Next, this dissertation introduces two novel programming systems. The first system (called Rocket) is designed to scale all-versus-all algorithms to massive datasets. It features a multi-level software-based cache, a divide-and-conquer approach, hierarchical work-stealing, and asynchronous processing to maximize data reuse, exploit data locality, dynamically balance workloads, and optimize resource utilization. The second system (called Lightning) aims to scale existing single-GPU kernel functions across multiple GPUs, even on different nodes, with minimal code adjustments. Results across eight benchmarks on up to 32 GPUs show excellent scalability.The dissertation concludes by proposing a set of design principles for developing parallel programming systems for scalable scientific computing. These principles, based on lessons from this PhD research, represent significant steps forward in enabling researchers to efficiently utilize HPC systems

Studies in Exascale Computer Architecture: Interconnect, Resiliency, and Checkpointing

Today’s supercomputers are built from the state-of-the-art components to extract as much performance as possible to solve the most computationally intensive problems in the world. Building the next generation of exascale supercomputers, however, would require re-architecting many of these components to extract over 50x more performance than the current fastest supercomputer in the United States. To contribute towards this goal, two aspects of the compute node architecture were examined in this thesis: the on-chip interconnect topology and the memory and storage checkpointing platforms. As a first step, a skeleton exascale system was modeled to meet 1 exaflop of performance along with 100 petabytes of main memory. The model revealed that large kilo-core processors would be necessary to meet the exaflop performance goal; existing topologies, however, would not scale to those levels. To address this new challenge, we investigated and proposed asymmetric high-radix topologies that decoupled local and global communications and used different radix routers for switching network traffic at each level. The proposed topologies scaled more readily to higher numbers of cores with better latency and energy consumption than before. The vast number of components that the model revealed would be needed in these exascale systems cautioned towards better fault tolerance mechanisms. To address this challenge, we showed that local checkpoints within the compute node can be saved to a hybrid DRAM and SSD platform in order to write them faster without wearing out the SSD or consuming a lot of energy. A hybrid checkpointing platform allowed more frequent checkpoints to be made without sacrificing performance. Subsequently, we proposed switching to a DIMM-based SSD in order to perform fine-grained I/O operations that would be integral in interleaving checkpointing and computation while still providing persistence guarantees. Two more techniques that consolidate and overlap checkpointing were designed to better hide the checkpointing latency to the SSD.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137096/1/sabeyrat_1.pd

Radio-Astronomical Imaging on Accelerators

Imaging is considered the most compute-intensive and therefore most challenging part of a radio-astronomical data-processing pipeline. To reach the high dynamic ranges imposed by the high sensitivity and large field of view of the new generation of radio telescopes such as the Square Kilometre Array (SKA), we need to be able to correct for direction-independent effects (DIEs) such as the curvature of the earth as well as for direction-dependent time-varying effects (DDEs) such as those caused by the ionosphere during imaging. The novel Image-Domain gridding (IDG) algorithm was designed to avoid the performance bottlenecks of traditional imaging algorithms. We implement, optimize, and analyze the performance and energy efficiency of IDG on a variety of hardware platforms to find which platform is the best for IDG. We analyze traditional CPUs, as well as several accelerators architectures. IDG alleviates the limitations of traditional imaging algorithms while it enables the advantages of GPU acceleration: better performance at lower power consumption. The hardware-software co-design has resulted in a highly efficient imager. This makes IDG on GPUs an ideal candidate for meeting the computational and energy efficiency constraints of the SKA. IDG has been integrated with a widely-used astronomical imager (WSClean) and is now being used in production by a variety of different radio observatories such as LOFAR and the MWA. It is not only faster and more energy-efficient than its competitors, but it also produces better quality images

Etude de l'adéquation des machines Exascale pour les algorithmes implémentant la méthode du Reverse Time Migation

As we are expecting Exascale systems for the 2018-2020 time frame, performance analysis and characterization of applications for new processor architectures and large scale systems are important tasks that permit to anticipate the required changes to efficiently exploit the future HPC systems. This thesis focuses on seismic imaging applications used for modeling complex physical phenomena, in particular the depth imaging application called Reverse Time Migration (RTM). My first contribution consists in characterizing and modeling the performance of the computational core of RTM which is based on finite-difference time-domain (FDTD) computations. I identify and explore the major tuning parameters influencing performance and the interaction between the architecture and the application. The second contribution is an analysis to identify the challenges for a hybrid and heterogeneous implementation of FDTD for manycore architectures. We target Intel’s first Xeon Phi co-processor, the Knights Corner. This architecture is an interesting proxy for our study since it contains some of the expected features of an Exascale system: concurrency and heterogeneity.My third contribution is an extension of the performance analysis and modeling to the full RTM. This adds communications and IOs to the computation part. RTM is a data intensive application and requires the storage of intermediate values of the computational field resulting in expensive IO accesses. My fourth contribution is the final measurement and model validation of my hybrid RTM implementation on a large system. This has been done on Stampede, a machine of the Texas Advanced Computing Center (TACC), which allows us to test the scalability up to 64 nodes each containing one 61-core Xeon Phi and two 8-core CPUs for a total close to 5000 heterogeneous coresLa caractérisation des applications en vue de les préparer pour les nouvelles architectures et les porter sur des systèmes très étendus est une étape importante pour pouvoir anticiper les modifications nécessaires. Comme les machines Exascale sont prévues pour la période 2018-2020, l'étude des applications et leur préparation pour ces machines s'avèrent donc essentielles. Nous nous intéressons aux applications d'imagerie sismique et en particulier à l'application Reverse Time Migration (RTM) car elle est très utilisée par les pétroliers dans le cadre de l'exploration sismique.La première partie de nos travaux a porté sur l'étude du cœur de calcul de l'application RTM qui consiste en un calcul de différences finies dans le domaine temporel (FDTD). Nous avons caractérisé cette partie de l'application en soulevant les aspects architecturaux des machines actuelles ayant un fort impact sur la performance, notamment les caches, les bandes passantes et le prefetching. Cette étude a abouti à l'élaboration d'un modèle de performance permettant de prédire le trafic DRAM des FDTD. La deuxième partie de la thèse se focalise sur l'impact de l'hétérogénéité et le parallélisme sur la FDTD et sur RTM. Nous avons choisi l'architecture manycore d’Intel, Xeon Phi, et nous avons étudié une implémentation "native" et une implémentation hétérogène et hybride, la version "symmetric". Enfin, nous avons porté l'application RTM sur un cluster hétérogène, Stampede du Texas Advanced Computing Center (TACC), où nous avons effectué des tests de scalabilité allant jusqu'à 64 nœuds contenant des coprocesseurs Xeon Phi et des processeurs Sandy Bridge ce qui correspond à presque 5000 cœur