On the Enhancement of Remote GPU Virtualization in High Performance Clusters

Graphics Processing Units (GPUs) are being adopted in many computing facilities given their extraordinary computing power, which makes it possible to accelerate many general purpose applications from different domains. However, GPUs also present several side effects, such as increased acquisition costs as well as larger space requirements. They also require more powerful energy supplies. Furthermore, GPUs still consume some amount of energy while idle and their utilization is usually low for most workloads. In a similar way to virtual machines, the use of virtual GPUs may address the aforementioned concerns. In this regard, the remote GPU virtualization mechanism allows an application being executed in a node of the cluster to transparently use the GPUs installed at other nodes. Moreover, this technique allows to share the GPUs present in the computing facility among the applications being executed in the cluster. In this way, several applications being executed in different (or the same) cluster nodes can share one or more GPUs located in other nodes of the cluster. Sharing GPUs should increase overall GPU utilization, thus reducing the negative impact of the side effects mentioned before. Reducing the total amount of GPUs installed in the cluster may also be possible. In this dissertation we enhance one framework offering remote GPU virtualization capabilities, referred to as rCUDA, for its use in high-performance clusters. While the initial prototype version of rCUDA demonstrated its functionality, it also revealed concerns with respect to usability, performance, and support for new GPU features, which prevented its used in production environments. These issues motivated this thesis, in which all the research is primarily conducted with the aim of turning rCUDA into a production-ready solution for eventually transferring it to industry. The new version of rCUDA resulting from this work presents a reduction of up to 35% in execution time of the applications analyzed with respect to the initial version. Compared to the use of local GPUs, the overhead of this new version of rCUDA is below 5% for the applications studied when using the latest high-performance computing networks available.Las unidades de procesamiento gráfico (Graphics Processing Units, GPUs) están siendo utilizadas en muchas instalaciones de computación dada su extraordinaria capacidad de cálculo, la cual hace posible acelerar muchas aplicaciones de propósito general de diferentes dominios. Sin embargo, las GPUs también presentan algunas desventajas, como el aumento de los costos de adquisición, así como mayores requerimientos de espacio. Asimismo, también requieren un suministro de energía más potente. Además, las GPUs consumen una cierta cantidad de energía aún estando inactivas, y su utilización suele ser baja para la mayoría de las cargas de trabajo. De manera similar a las máquinas virtuales, el uso de GPUs virtuales podría hacer frente a los inconvenientes mencionados. En este sentido, el mecanismo de virtualización remota de GPUs permite que una aplicación que se ejecuta en un nodo de un clúster utilice de forma transparente las GPUs instaladas en otros nodos de dicho clúster. Además, esta técnica permite compartir las GPUs presentes en el clúster entre las aplicaciones que se ejecutan en el mismo. De esta manera, varias aplicaciones que se ejecutan en diferentes nodos de clúster (o los mismos) pueden compartir una o más GPUs ubicadas en otros nodos del clúster. Compartir GPUs aumenta la utilización general de la GPU, reduciendo así el impacto negativo de las desventajas anteriormente mencionadas. De igual forma, este mecanismo también permite reducir la cantidad total de GPUs instaladas en el clúster. En esta tesis mejoramos un entorno de trabajo llamado rCUDA, el cual ofrece funcionalidades de virtualización remota de GPUs para su uso en clusters de altas prestaciones. Si bien la versión inicial del prototipo de rCUDA demostró su funcionalidad, también reveló dificultades con respecto a la usabilidad, el rendimiento y el soporte para nuevas características de las GPUs, lo cual impedía su uso en entornos de producción. Estas consideraciones motivaron la presente tesis, en la que toda la investigación llevada a cabo tiene como objetivo principal convertir rCUDA en una solución lista para su uso entornos de producción, con la finalidad de transferirla eventualmente a la industria. La nueva versión de rCUDA resultante de este trabajo presenta una reducción de hasta el 35% en el tiempo de ejecución de las aplicaciones analizadas con respecto a la versión inicial. En comparación con el uso de GPUs locales, la sobrecarga de esta nueva versión de rCUDA es inferior al 5% para las aplicaciones estudiadas cuando se utilizan las últimas redes de computación de altas prestaciones disponibles.Les unitats de processament gràfic (Graphics Processing Units, GPUs) estan sent utilitzades en moltes instal·lacions de computació donada la seva extraordinària capacitat de càlcul, la qual fa possible accelerar moltes aplicacions de propòsit general de diferents dominis. No obstant això, les GPUs també presenten alguns desavantatges, com l'augment dels costos d'adquisició, així com major requeriment d'espai. Així mateix, també requereixen un subministrament d'energia més potent. A més, les GPUs consumeixen una certa quantitat d'energia encara estant inactives, i la seua utilització sol ser baixa per a la majoria de les càrregues de treball. D'una manera semblant a les màquines virtuals, l'ús de GPUs virtuals podria fer front als inconvenients esmentats. En aquest sentit, el mecanisme de virtualització remota de GPUs permet que una aplicació que s'executa en un node d'un clúster utilitze de forma transparent les GPUs instal·lades en altres nodes d'aquest clúster. A més, aquesta tècnica permet compartir les GPUs presents al clúster entre les aplicacions que s'executen en el mateix. D'aquesta manera, diverses aplicacions que s'executen en diferents nodes de clúster (o els mateixos) poden compartir una o més GPUs ubicades en altres nodes del clúster. Compartir GPUs augmenta la utilització general de la GPU, reduint així l'impacte negatiu dels desavantatges anteriorment esmentades. A més a més, aquest mecanisme també permet reduir la quantitat total de GPUs instal·lades al clúster. En aquesta tesi millorem un entorn de treball anomenat rCUDA, el qual ofereix funcionalitats de virtualització remota de GPUs per al seu ús en clústers d'altes prestacions. Si bé la versió inicial del prototip de rCUDA va demostrar la seua funcionalitat, també va revelar dificultats pel que fa a la usabilitat, el rendiment i el suport per a noves característiques de les GPUs, la qual cosa impedia el seu ús en entorns de producció. Aquestes consideracions van motivar la present tesi, en què tota la investigació duta a terme té com a objectiu principal convertir rCUDA en una solució preparada per al seu ús entorns de producció, amb la finalitat de transferir-la eventualment a la indústria. La nova versió de rCUDA resultant d'aquest treball presenta una reducció de fins al 35% en el temps d'execució de les aplicacions analitzades respecte a la versió inicial. En comparació amb l'ús de GPUs locals, la sobrecàrrega d'aquesta nova versió de rCUDA és inferior al 5% per a les aplicacions estudiades quan s'utilitzen les últimes xarxes de computació d'altes prestacions disponibles.Reaño González, C. (2017). On the Enhancement of Remote GPU Virtualization in High Performance Clusters [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86219TESISPremios Extraordinarios de tesis doctorale

PoCL-R : A Scalable Low Latency Distributed OpenCL Runtime

Offloading the most demanding parts of applications to an edge GPU server cluster to save power or improve the result quality is a solution that becomes increasingly realistic with new networking technologies. In order to make such a computing scheme feasible, an application programming layer that can provide both low latency and scalable utilization of remote heterogeneous computing resources is needed. To this end, we propose a latency-optimized scalable distributed heterogeneous computing runtime implementing the standard OpenCL API. In the proposed runtime, network-induced latency is reduced by means of peer-to-peer data transfers and event synchronization as well as a streamlined control protocol implementation. Further improvements can be obtained streaming of source data directly from the producer device to the compute cluster. Compute cluster scalability is improved by distributing the command and event processing responsibilities to remote compute servers. We also show how a simple optional dynamic content size buffer OpenCL extension can significantly speed up applications that utilize variable length data. For evaluation we present a smartphone-based augmented reality rendering case study which, using the runtime, receives 19× improvement in frames per second and 17× improvement in energy per frame when offloading parts of the rendering workload to a nearby GPU server. The remote kernel execution latency overhead of the runtime is only 60 ms on top of the network roundtrip time. The scalability on multi-server multi-GPU clusters is shown with a distributed large matrix multiplication application.acceptedVersionPeer reviewe

On the programmability of multi-GPU computing systems

Multi-GPU systems are widely used in High Performance Computing environments to accelerate scientific computations. This trend is expected to continue as integrated GPUs will be introduced to processors used in multi-socket servers and servers will pack a higher number of GPUs per node. GPUs are currently connected to the system through the PCI Express interconnect, which provides limited bandwidth (compared to the bandwidth of the memory in GPUs) and it often becomes a bottleneck for performance scalability. Current programming models present GPUs as isolated devices with their own memory, even if they share the host memory with the CPU. Programmers explicitly manage allocations in all GPU memories and use primitives to communicate data between GPUs. Furthermore, programmers are required to use mechanisms such as command queues and inter-GPU synchronization. This explicit model harms the maintainability of the code and introduces new sources for potential errors. The first proposal of this thesis is the HPE model. HPE builds a simple, consistent programming interface based on three major features. (1) All device address spaces are combined with the host address space to form a Unified Virtual Address Space. (2) Programs are provided with an Asymmetric Distributed Shared Memory system for all the GPUs in the system. It allows to allocate memory objects that can be accessed by any GPU or CPU. (3) Every CPU thread can request a data exchange between any two GPUs, through simple memory copy calls. Such a simple interface allows HPE to provide always the optimal implementation; eliminating the need for application code to handle different system topologies. Experimental results show improvements on real applications that range from 5% in compute-bound benchmarks to 2.6x in communication-bound benchmarks. HPE transparently implements sophisticated communication schemes that can deliver up to a 2.9x speedup in I/O device transfers. The second proposal of this thesis is a shared memory programming model that exploits the new GPU capabilities for remote memory accesses to remove the need for explicit communication between GPUs. This model turns a multi-GPU system into a shared memory system with NUMA characteristics. In order to validate the viability of the model we also perform an exhaustive performance analysis of remote memory accesses over PCIe. We show that the unique characteristics of the GPU execution model and memory hierarchy help to hide the costs of remote memory accesses. Results show that PCI Express 3.0 is able to hide the costs of up to a 10% of remote memory accesses depending on the access pattern, while caching of remote memory accesses can have a large performance impact on kernel performance. Finally, we introduce AMGE, a programming interface, compiler support and runtime system that automatically executes computations that are programmed for a single GPU across all the GPUs in the system. The programming interface provides a data type for multidimensional arrays that allows for robust, transparent distribution of arrays across all GPU memories. The compiler extracts the dimensionality information from the type of each array, and is able to determine the access pattern in each dimension of the array. The runtime system uses the compiler-provided information to automatically choose the best computation and data distribution configuration to minimize inter-GPU communication and memory footprint. This model effectively frees programmers from the task of decomposing and distributing computation and data to exploit several GPUs. AMGE achieves almost linear speedups for a wide range of dense computation benchmarks on a real 4-GPU system with an interconnect with moderate bandwidth. We show that irregular computations can also benefit from AMGE, too.Los sistemas multi-GPU son muy comúnmente utilizados en entornos de computación de altas prestaciones para acelerar cálculos científicos. Esta tendencia continuará con la introducción de GPUs integradas en los procesadores de los servidores procesador y con una mayor densidad de GPUs por nodo. Las GPUs actualmente se contectan al sistema a través de una interconexión PCI Express, que provee un ancho de banda reducido (comparado con las memorias de las GPUs) y habitualmente se convierte en el cuello de botella para escalar el rendimiento. Los modelos de programación actuales exponen las GPUs como dispositivos aislados con su propia memoria, incluso si comparten la memoria física con la CPU. Los programadores manejan diferentes reservas en todas las memorias de GPU y usan primitivas para comunicar datos entre GPUs. Además, los programadores deben utilizar mecanismos como colas de comandos y sincronicación entre GPUs. Este modelo explícito empeora la programabilidad del código e introduce nuevas fuentes de errores potenciales. La primera propuesta de esta tesis es el modelo HPE. HPE construye una interfaz de programaci ón consistente basada en tres características principales. (1) Todos los espacios de direcciones de los dispositivos son combinados para formar un espacio de direcciones unificado. (2) Los programas usan un sistema asimétrico distribuido de memoria compartida para todas las GPUs del sistema, que permite declarar objetos de memoria que pueden ser accedidos por cualquier GPU o CPU. (3) Cada hilo de ejecución de la CPU puede lanzar un intercambio de datos entre dos GPUs a través de simples llamadas de copia de memoria. Esta interfaz simplificada permite a HPE usar la implementaci ón óptima; sinque la aplicación contemple diferentes topologías de sistema. Los resultados experimentales muestran mejoras en aplicaciones reales que van desde un 5% en aplicaciones limitadas por el cómputo a 2.6x aplicaciones imitadas por la comunicación. HPE implementa sofisticados esquemas de transferencia para dispositivos de E/S que proporcionan mejoras de rendimiento de 2.9x. La segunda propuesta de esta tesis es un modelo de programación basado en memoria compartida que aprovecha las nuevas capacidades acceso remoto de memoria de las GPUs para eliminar la comunicación explícita entre memorias de GPU. Este modelo convierte un sistema multi-GPU en un sistema de memoria compartida con características NUMA. Para validar la viabilidad del modelo realizamos un anlásis exhaustivo del rendimiento los accessos de memoria remotos sobre PCIe. Los resultados muestran que PCI Express 3.0 elimina los costes de hasta un 10% de accesos remotos, dependiendo en el patrón de acceso, mientras que guardar los accesos remotos en memorias cache tiene un gran inpacto en el rendimiento de las computaciones. Finalmente, presentamos AMGE, una interfaz de programación con soporte de compilación y un sistema que ejecuta, de forma automática, computaciones programadas para una única GPU en todas las GPUs del sistema. La interfaz de programación proporciona un tipo de datos para arreglos multidimensionales que permite una distribuci ón transparente y robusta de los datos en todas las memorias de GPU. El compilador extrae la información sobre la dimensionalidad de cada arreglo y puede determinar el patrón de acceso en cada dimensión de forma individual. El sistema utiliza, en tiempo de ejecución, la información del compilador para elegir la mejor descomposición de la computación y los datos para minimizar la comunicación entre GPUs y el uso de memoria. AMGE consigue mejoras de rendimiento que crecen de forma lineal con el número de GPUs para un amplio abanico de computaciones densas en un sistema real con 4 GPUs. También mostramos que las computaciones con patrones irregulares también se pueden beneficiar de AMGE

Real-Time Scheduling for GPUs with Applications in Advanced Automotive Systems

Self-driving cars, once constrained to closed test tracks, are beginning to drive alongside human drivers on public roads. Loss of life or property may result if the computing systems of automated vehicles fail to respond to events at the right moment. We call such systems that must satisfy precise timing constraints “real-time systems.” Since the 1960s, researchers have developed algorithms and analytical techniques used in the development of real-time systems; however, this body of knowledge primarily applies to traditional CPU-based platforms. Unfortunately, traditional platforms cannot meet the computational requirements of self-driving cars without exceeding the power and cost constraints of commercially viable vehicles. We argue that modern graphics processing units, or GPUs, represent a feasible alternative, but new algorithms and analytical techniques must be developed in order to integrate these uniquely constrained processors into a real-time system. The goal of the research presented in this dissertation is to discover and remedy the issues that prevent the use of GPUs in real-time systems. To overcome these issues, we design and implement a real-time multi-GPU scheduler, called GPUSync. GPUSync tightly controls access to a GPU’s computational and DMA processors, enabling simultaneous use despite potential limitations in GPU hardware. GPUSync enables tasks to migrate among GPUs, allowing new classes of real-time multi-GPU computing platforms. GPUSync employs heuristics to guide scheduling decisions to improve system efficiency without risking violations in real-time constraints. GPUSync may be paired with a wide variety of common real-time CPU schedulers. GPUSync supports closed-source GPU runtimes and drivers without loss in functionality. We evaluate GPUSync with both analytical and runtime experiments. In our analytical experiments, we model and evaluate over fifty configurations of GPUSync. We determine which configurations support the greatest computational capacity while maintaining real-time constraints. In our runtime experiments, we execute computer vision programs similar to those found in automated vehicles, with and without GPUSync. Our results demonstrate that GPUSync greatly reduces jitter in video processing. Research into real-time systems with GPUs is a new area of study. Although there is prior work on such systems, no other GPU scheduling framework is as comprehensive and flexible as GPUSync.Doctor of Philosoph

Improving the performance of physics applications in atom-based clusters with rCUDA

[EN] Traditionally, High-Performance Computing (HPC) has been associated with large power requirements. The reason was that chip makers of the processors typically employed in HPC deployments have always focused on getting the highest performance from their designs, regardless of the energy their processors may consume. Actually, for many years only heat dissipation was the real barrier for achieving higher performance, at the cost of higher energy consumption. However, a new trend has recently appeared consisting on the use of low-power processors for HPC purposes. The MontBlanc and Isambard projects are good examples of this trend. These proposals, however, do not consider the use of GPUs. In this paper we propose to use GPUs in this kind of low-power processor based HPC deployments by making use of the remote GPU virtualization mechanism. To that end, we leverage the rCUDA middleware in a hybrid cluster composed of low-power Atom-based nodes and regular Xeon-based nodes equipped with GPUs. Our experiments show that, by making use of rCUDA, the execution time of applications belonging to the physics domain is noticeably reduced, achieving a speed up of up to 140x with just one remote NVIDIA V100 GPU with respect to the execution of the same applications using 8 Atom-based nodes. Additionally, a rough energy consumption estimation reports improvements in energy demands of up to 37x. (C) 2019 Elsevier Inc. All rights reserved.This work was funded by the Generalitat Valenciana, Spain under Grant PROMETEO/2017/077. Authors are also grateful for the generous support provided by Mellanox Technologies Inc.Silla, F.; Prades, J.; Baydal Cardona, ME.; Reaño, C. (2020). Improving the performance of physics applications in atom-based clusters with rCUDA. Journal of Parallel and Distributed Computing. 137:160-178. https://doi.org/10.1016/j.jpdc.2019.11.007S160178137R.E. Brown, E.R. Masanet, B. Nordman, W.F. Tschudi, A. Shehabi, J. Stanley, J.G. Koomey, D.A. Sartor, P.T. Chan, Report to congress on server and data center energy efficiency: public law 109-431, Berkeley, CA, 2008.G. Giunta, R. Montella, G. Agrillo, G. Coviello, A GPGPU transparent virtualization component for high performance computing clouds, in: Proc. of the Euro-Par Parallel Processing, Euro-Par, 2010, pp. 379–391.V. Gupta, A. Gavrilovska, K. Schwan, H. Kharche, N. Tolia, V. Talwar, P. Ranganathan, GViM: GPU-accelerated virtual machines, in: Proc. of the ACM Workshop on System-Level Virtualization for High Performance Computing, HPCVirt, 2009, pp. 17–24.J.A. Herdman, W.P. Gaudin, S. McIntosh-Smith, M. Boulton, D.A. Beckingsale, A.C. Mallinson, S.A. Jarvis, Accelerating hydrocodes with OpenACC, OpenCL and CUDA, in: 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, 2012.Koomey, J. G. (2008). Worldwide electricity used in data centers. Environmental Research Letters, 3(3), 034008. doi:10.1088/1748-9326/3/3/034008T.Y. Liang, Y.W. Chang, GridCuda: A grid-enabled CUDA programming toolkit, in: Proc. of the IEEE Advanced Information Networking and Applications Workshops, WAINA, 2011, pp. 141–146.Maqbool, J., Oh, S., & Fox, G. C. (2015). Evaluating ARM HPC clusters for scientific workloads. Concurrency and Computation: Practice and Experience, 27(17), 5390-5410. doi:10.1002/cpe.3602M. Martineau, S. McIntosh-Smith, Exploring on-node parallelism with neutral, a Monte Carlo neutral particle transport mini-app, in: 2017 IEEE International Conference on Cluster Computing, CLUSTER, 2017.M. Martineau, S. McIntosh-Smith, The arch project: physics mini-apps for algorithmic exploration and evaluating programming environments on HPC architectures, in: 2017 IEEE International Conference on Cluster Computing, CLUSTER, 2017.M. Martineau, S. McIntosh-Smith, M. Boulton, W. Gaudin, An evaluation of emerging many-core parallel programming models, in: Proceedings of the 7th International Workshop on Programming Models and Applications for Multicores and Manycores, PMAM’16, 2016.M. Oikawa, A. Kawai, K. Nomura, K. Yasuoka, K. Yoshikawa, T. Narumi, DS-CUDA: A middleware to use many GPUs in the cloud environment, in: Proc. of the SC Companion: High Performance Computing, Networking Storage and Analysis, SCC, 2012, pp. 1207–1214.Prades, J., Reaño, C., & Silla, F. (2018). On the effect of using rCUDA to provide CUDA acceleration to Xen virtual machines. Cluster Computing, 22(1), 185-204. doi:10.1007/s10586-018-2845-0Prades, J., Varghese, B., Reaño, C., & Silla, F. (2017). Multi-tenant virtual GPUs for optimising performance of a financial risk application. Journal of Parallel and Distributed Computing, 108, 28-44. doi:10.1016/j.jpdc.2016.06.002N. Rajovic, et al. The Mont-Blanc prototype: an alternative approach for HPC systems, in: SC16: International Conference for High Performance Computing, Networking, Storage and Analysis, 2016, pp. 444–455.C. Reaño, F. Silla, A performance comparison of CUDA remote GPU virtualization frameworks, in: 2015 IEEE International Conference on Cluster Computing, 2015.C. Reaño, F. Silla, Extending rCUDA with support for P2P memory copies between remote GPUs, in: 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems, HPCC/SmartCity/DSS, 2016.C. Reaño, F. Silla, J. Duato, Enhancing the rCUDA remote GPU virtualization framework: From a prototype to a production solution, in: Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid ’17, 2017.C. Reaño, F. Silla, G. Shainer, S. Schultz, Local and remote GPUs perform similar with EDR 100G InfiniBand, in: Proceedings of the Industrial Track of the 16th International Middleware Conference, Middleware Industry ’15, 2015.A. Selinger, K. Rupp, S. Selberherr, Evaluation of mobile ARM-based SoCs for high performance computing, in: Proceedings of the 24th High Performance Computing Symposium, HPC ’16, 2016, pp. 21:1–21:7.L. Shi, H. Chen, J. Sun, vCUDA: GPU accelerated high performance computing in virtual machines, in: Proc. of the IEEE Parallel and Distributed Processing Symposium, IPDPS, 2009, pp. 1–11.F. Silla, J. Prades, S. Iserte, C. Reaño, Remote GPU virtualization: is it useful?, in: 2016 2nd IEEE International Workshop on High-Performance Interconnection Networks in the Exascale and Big-Data Era, HiPINEB, 2016.F. Silla, J. Prades, C. Reaño, Leveraging rCUDA for enhancing low-power deployments in the physics domain, in: Proceedings of the 47th International Conference on Parallel Processing Companion, ICPP ’18, 2018