SKIRT: hybrid parallelization of radiative transfer simulations

We describe the design, implementation and performance of the new hybrid parallelization scheme in our Monte Carlo radiative transfer code SKIRT, which has been used extensively for modeling the continuum radiation of dusty astrophysical systems including late-type galaxies and dusty tori. The hybrid scheme combines distributed memory parallelization, using the standard Message Passing Interface (MPI) to communicate between processes, and shared memory parallelization, providing multiple execution threads within each process to avoid duplication of data structures. The synchronization between multiple threads is accomplished through atomic operations without high-level locking (also called lock-free programming). This improves the scaling behavior of the code and substantially simplifies the implementation of the hybrid scheme. The result is an extremely flexible solution that adjusts to the number of available nodes, processors and memory, and consequently performs well on a wide variety of computing architectures.Comment: 21 pages, 20 figure

Task-based adaptive multiresolution for time-space multi-scale reaction-diffusion systems on multi-core architectures

A new solver featuring time-space adaptation and error control has been recently introduced to tackle the numerical solution of stiff reaction-diffusion systems. Based on operator splitting, finite volume adaptive multiresolution and high order time integrators with specific stability properties for each operator, this strategy yields high computational efficiency for large multidimensional computations on standard architectures such as powerful workstations. However, the data structure of the original implementation, based on trees of pointers, provides limited opportunities for efficiency enhancements, while posing serious challenges in terms of parallel programming and load balancing. The present contribution proposes a new implementation of the whole set of numerical methods including Radau5 and ROCK4, relying on a fully different data structure together with the use of a specific library, TBB, for shared-memory, task-based parallelism with work-stealing. The performance of our implementation is assessed in a series of test-cases of increasing difficulty in two and three dimensions on multi-core and many-core architectures, demonstrating high scalability

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs because of three challenges: (1) the difficulty of coming up with graph building blocks, (2) load imbalance on parallel hardware, and (3) graph problems having low arithmetic intensity. To address some of these challenges, GraphBLAS is an innovative, on-going effort by the graph analytics community to propose building blocks based on sparse linear algebra, which will allow graph algorithms to be expressed in a performant, succinct, composable and portable manner. In this paper, we examine the performance challenges of a linear-algebra-based approach to building graph frameworks and describe new design principles for overcoming these bottlenecks. Among the new design principles is exploiting input sparsity, which allows users to write graph algorithms without specifying push and pull direction. Exploiting output sparsity allows users to tell the backend which values of the output in a single vectorized computation they do not want computed. Load-balancing is an important feature for balancing work amongst parallel workers. We describe the important load-balancing features for handling graphs with different characteristics. The design principles described in this paper have been implemented in "GraphBLAST", the first high-performance linear algebra-based graph framework on NVIDIA GPUs that is open-source. The results show that on a single GPU, GraphBLAST has on average at least an order of magnitude speedup over previous GraphBLAS implementations SuiteSparse and GBTL, comparable performance to the fastest GPU hardwired primitives and shared-memory graph frameworks Ligra and Gunrock, and better performance than any other GPU graph framework, while offering a simpler and more concise programming model.Comment: 50 pages, 14 figures, 14 table

On the maturity of parallel applications for asymmetric multi-core processors

Asymmetric multi-cores (AMCs) are a successful architectural solution for both mobile devices and supercomputers. By maintaining two types of cores (fast and slow) AMCs are able to provide high performance under the facility power budget. This paper performs the first extensive evaluation of how portable are the current HPC applications for such supercomputing systems. Specifically we evaluate several execution models on an ARM big.LITTLE AMC using the PARSEC benchmark suite that includes representative highly parallel applications. We compare schedulers at the user, OS and runtime levels, using both static and dynamic options and multiple configurations, and assess the impact of these options on the well-known problem of balancing the load across AMCs. Our results demonstrate that scheduling is more effective when it takes place in the runtime system level as it improves the baseline by 23%, while the heterogeneous-aware OS scheduling solution improves the baseline by 10%.This work has been supported by the RoMoL ERC Advanced Grant (GA 321253), by the European HiPEAC Network of Excellence, by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P), by the Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), and by the European Union's Horizon 2020 research and innovation programme under grant agreement No 671697 and No. 779877. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Ramon y Cajal fellowship number RYC-2016-21104.Peer ReviewedPostprint (author's final draft

Energy Saving and Virtualization Technologies in Switching

Switching is the key functionality for many devices like electronic Router and Switch, optical Router, Network on Chips (NoCs) and so on. Basically, switching is responsible for moving data unit from one port/location to another (or multiple) port(s)/location(s). In past years, the high capacity, low delay were the main concerns when designing high-end switching unit. As new demands, requests and technologies emerge, flexibility and low power cost switching design become to weight the same as throughput and delay. On one hand, highly flexible (i.e, programming ability) switching can cope with variable needs stem from new applications (i.e, VoIP) and popular user behavior (i.e, p2p downloading); on the other hand, reduce the energy and power dissipation for switching could not only save bills and build echo system but also expand components life time. Many research efforts have been devoted to increase switching flexibility and reduce its power cost. In this thesis work, we consider to exploit virtualization as the main technique to build flexible software router in the first part, then in the second part we draw our attention on energy saving in NoC (i.e, a switching fabric designed to handle the on chip data transmission) and software router. In the first part of the thesis, we consider the virtualization inside Software Routers (SRs). SR, i.e, routers running in commodity Personal Computers (PCs), become an appealing solution compared to traditional Proprietary Routing Devices (PRD) for various reasons such as cost (the multi-vendor hardware used by SRs can be cheap, while the equipment needed by PRDs is more expensive and their training cost is higher), openness (SRs can make use of a large number of open source networking applications, while PRDs are more closed) and flexibility. The forwarding performance provided by SRs has been an obstacle to their deployment in real networks. For this reason, we proposed to aggregate multiple routing units that form an powerful SR known as the Multistage Software Router (MSR) to overcome the performance limitation for a single SR. Our results show that the throughput can increase almost linearly as the number of the internal routing devices. But some other features related to flexibility (such as power saving, programmability, router migration or easy management) have been investigated less than performance previously. We noticed that virtualization techniques become reality thanks to the quick development of the PC architectures, which are now able to easily support several logical PCs running in parallel on the same hardware. Virtualization could provide many flexible features like hardware and software decoupling, encapsulation of virtual machine state, failure recovery and security, to name a few. Virtualization permits to build multiple SRs inside one physical host and a multistage architecture exploiting only logical devices. By doing so, physical resources can be used in a more efficient way, energy savings features (switching on and off device when needed) can be introduced and logical resources could be rented on-demand instead of being owned. Since virtualization techniques are still difficult to deploy, several challenges need to be faced when trying to integrate them into routers. The main aim of the first part in this thesis is to find out the feasibility of the virtualization approach, to build and test virtualized SR (VSR), to implement the MSR exploiting logical, i.e. virtualized, resources, to analyze virtualized routing performance and to propose improvement techniques to VSR and virtual MSR (VMSR). More specifically, we considered different virtualization solutions like VMware, XEN, KVM to build VSR and VMSR, being VMware a closed source solution but with higher performance and XEN/KVM open source solutions. Firstly we built and tested each single component of our multistage architecture (i.e, back-end router, load balancer )inside the virtual infrastructure, then and we extended the performance experiments with more complex scenarios like multiple Back-end Router (BR) or Load Balancer (LB) which cooperate to route packets. Our results show that virtualization could introduce 40~\% performance penalty compare with the hardware only solution. Keep the performance limitation in mind, we developed the whole VMSR and we obtained low throughput with 64B packet flow as expected. To increase the VMSR throughput, two directions could be considered, the first one is to improve the single component ( i.e, VSR) performance and the other is to work from the topology (i.e, best allocation of the VMs into the hardware ) point of view. For the first method, we considered to tune the VSR inside the KVM and we studied closely such as Linux driver, scheduler, interconnect methodology which could impact the performance significantly with proper configuration; then we proposed two ways for the VMs allocation into physical servers to enhance the VMSR performance. Our results show that with good tuning and allocation of VMs, we could minimize the virtualization penalty and get reasonable throughput for running SRs inside virtual infrastructure and add flexibility functionalities into SRs easily. In the second part of the thesis, we consider the energy efficient switching design problem and we focus on two main architecture, the NoC and MSR. As many research works suggest, the energy cost in the Communication Technologies ( ICT ) is constantly increasing. Among the main ICT sectors, a large portion of the energy consumption is contributed by the telecommunication infrastructure and their devices, i.e, router, switch, cell phone, ip TV settle box, storage home gateway etc. More in detail, the linecards, links, System on Chip (SoC) including the transmitter/receiver on these variate devices are the main power consuming units. We firstly present the work on the power reduction of the data transmission in SoC, which is carried out by the NoC. NoC is an approach to design the communication subsystem between different Processing Units (PEs) in a SoC. PEs could be different elements such as CPU, memory, digital signal/analog signal processor etc. Different PEs performs specific tasks depending on the applications running on the chip. Different tasks need to exchange data information among each other, thus flits ( chopped packet with limited header information ) are generated by PEs. The flits are injected into the NoC by the proper interface and routed until reach the destination PEs. For the whole procedure, the NoC behaves as a packet switch network. Studies show that in general the information processing in the PEs only consume 60~\% energy while the remaining 40~\% are consumed by the NoC. More importantly, as the current network designing principle, the NoC capacity is devised to handle the peak load. This is a clear sign for energy saving when the network load is low. In our work, we considered to exploit Dynamic Voltage and Frequency Scaling (DVFS) technique, which can jointly decrease or increase the system voltage and frequency when necessary, i.e, decrease the voltage and frequency at low load scenario to save energy and reduce power dissipation. More precisely, we studied two different NoC architectures for energy saving, namely single plane chip and multi-plane chip architecture. In both cases we have a very strict constraint to be that all the links and transmitter/receivers on the same plane work at the same frequency/voltage to avoid synchronization problem. This is the main difference with many existing works in the literature which usually assume different links can work at different frequency, that is hard to be implemented in reality. For the single plane NoC, we exploited different routing schemas combined with DVFS to reduce the power for the whole chip. Our results haven been compared with the optimal value obtained by modeling the power saving formally as a quadratic programming problem. Results suggest that just by using simple load balancing routing algorithm, we can save considerable energy for the single chip NoC architecture. Furthermore, we noticed that in the single plane NoC architecture, the bottleneck link could limit the DVFS effectiveness. Then we discovered that multiplane NoC architecture is fairly easy to be implemented and it could help with the energy saving. Thus we focus on the multiplane architecture and we found out that DVFS could be more efficient when we concentrate more traffic into one plane and send the remaining flows to other planes. We compared load concentration and load balancing with different power modeling and all simulation results show that load concentration is better compared with load balancing for multiplan NoC architecture. Finally, we also present one of the the energy efficient MSR design technique, which permits the MSR to follow the day-night traffic pattern more efficiently with our on-line energy saving algorithm

Hierarchical fractional-step approximations and parallel kinetic Monte Carlo algorithms

We present a mathematical framework for constructing and analyzing parallel algorithms for lattice Kinetic Monte Carlo (KMC) simulations. The resulting algorithms have the capacity to simulate a wide range of spatio-temporal scales in spatially distributed, non-equilibrium physiochemical processes with complex chemistry and transport micro-mechanisms. The algorithms can be tailored to specific hierarchical parallel architectures such as multi-core processors or clusters of Graphical Processing Units (GPUs). The proposed parallel algorithms are controlled-error approximations of kinetic Monte Carlo algorithms, departing from the predominant paradigm of creating parallel KMC algorithms with exactly the same master equation as the serial one. Our methodology relies on a spatial decomposition of the Markov operator underlying the KMC algorithm into a hierarchy of operators corresponding to the processors' structure in the parallel architecture. Based on this operator decomposition, we formulate Fractional Step Approximation schemes by employing the Trotter Theorem and its random variants; these schemes, (a) determine the communication schedule} between processors, and (b) are run independently on each processor through a serial KMC simulation, called a kernel, on each fractional step time-window. Furthermore, the proposed mathematical framework allows us to rigorously justify the numerical and statistical consistency of the proposed algorithms, showing the convergence of our approximating schemes to the original serial KMC. The approach also provides a systematic evaluation of different processor communicating schedules.Comment: 34 pages, 9 figure

Parallelizing Maximal Clique Enumeration on GPUs

We present a GPU solution for exact maximal clique enumeration (MCE) that performs a search tree traversal following the Bron-Kerbosch algorithm. Prior works on parallelizing MCE on GPUs perform a breadth-first traversal of the tree, which has limited scalability because of the explosion in the number of tree nodes at deep levels. We propose to parallelize MCE on GPUs by performing depth-first traversal of independent subtrees in parallel. Since MCE suffers from high load imbalance and memory capacity requirements, we propose a worker list for dynamic load balancing, as well as partial induced subgraphs and a compact representation of excluded vertex sets to regulate memory consumption. Our evaluation shows that our GPU implementation on a single GPU outperforms the state-of-the-art parallel CPU implementation by a geometric mean of 4.9x (up to 16.7x), and scales efficiently to multiple GPUs. Our code has been open-sourced to enable further research on accelerating MCE