Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

The efficient parallel execution of scientific applications is a key challenge in high-performance computing (HPC). With growing parallelism and heterogeneity of compute resources as well as increasingly complex software, performance analysis has become an indispensable tool in the development and optimization of parallel programs. This thesis presents a framework for systematic performance analysis of scalable, heterogeneous applications. Based on event traces, it automatically detects the critical path and inefficiencies that result in waiting or idle time, e.g. due to load imbalances between parallel execution streams. As a prerequisite for the analysis of heterogeneous programs, this thesis specifies inefficiency patterns for computation offloading. Furthermore, an essential contribution was made to the development of tool interfaces for OpenACC and OpenMP, which enable a portable data acquisition and a subsequent analysis for programs with offload directives. At present, these interfaces are already part of the latest OpenACC and OpenMP API specification. The aforementioned work, existing preliminary work, and established analysis methods are combined into a generic analysis process, which can be applied across programming models. Based on the detection of wait or idle states, which can propagate over several levels of parallelism, the analysis identifies wasted computing resources and their root cause as well as the critical-path share for each program region. Thus, it determines the influence of program regions on the load balancing between execution streams and the program runtime. The analysis results include a summary of the detected inefficiency patterns and a program trace, enhanced with information about wait states, their cause, and the critical path. In addition, a ranking, based on the amount of waiting time a program region caused on the critical path, highlights program regions that are relevant for program optimization. The scalability of the proposed performance analysis and its implementation is demonstrated using High-Performance Linpack (HPL), while the analysis results are validated with synthetic programs. A scientific application that uses MPI, OpenMP, and CUDA simultaneously is investigated in order to show the applicability of the analysis

Intelligent Scheduling and Memory Management Techniques for Modern GPU Architectures

abstract: With the massive multithreading execution feature, graphics processing units (GPUs) have been widely deployed to accelerate general-purpose parallel workloads (GPGPUs). However, using GPUs to accelerate computation does not always gain good performance improvement. This is mainly due to three inefficiencies in modern GPU and system architectures. First, not all parallel threads have a uniform amount of workload to fully utilize GPU’s computation ability, leading to a sub-optimal performance problem, called warp criticality. To mitigate the degree of warp criticality, I propose a Criticality-Aware Warp Acceleration mechanism, called CAWA. CAWA predicts and accelerates the critical warp execution by allocating larger execution time slices and additional cache resources to the critical warp. The evaluation result shows that with CAWA, GPUs can achieve an average of 1.23x speedup. Second, the shared cache storage in GPUs is often insufficient to accommodate demands of the large number of concurrent threads. As a result, cache thrashing is commonly experienced in GPU’s cache memories, particularly in the L1 data caches. To alleviate the cache contention and thrashing problem, I develop an instruction aware Control Loop Based Adaptive Bypassing algorithm, called Ctrl-C. Ctrl-C learns the cache reuse behavior and bypasses a portion of memory requests with the help of feedback control loops. The evaluation result shows that Ctrl-C can effectively improve cache utilization in GPUs and achieve an average of 1.42x speedup for cache sensitive GPGPU workloads. Finally, GPU workloads and the co-located processes running on the host chip multiprocessor (CMP) in a heterogeneous system setup can contend for memory resources in multiple levels, resulting in significant performance degradation. To maximize the system throughput and balance the performance degradation of all co-located applications, I design a scalable performance degradation predictor specifically for heterogeneous systems, called HeteroPDP. HeteroPDP predicts the application execution time and schedules OpenCL workloads to run on different devices based on the optimization goal. The evaluation result shows HeteroPDP can improve the system fairness from 24% to 65% when an OpenCL application is co-located with other processes, and gain an additional 50% speedup compared with always offloading the OpenCL workload to GPUs. In summary, this dissertation aims to provide insights for the future microarchitecture and system architecture designs by identifying, analyzing, and addressing three critical performance problems in modern GPUs.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

Domain-specific Architectures for Data-intensive Applications

Graphs' versatile ability to represent diverse relationships, make them effective for a wide range of applications. For instance, search engines use graph-based applications to provide high-quality search results. Medical centers use them to aid in patient diagnosis. Most recently, graphs are also being employed to support the management of viral pandemics. Looking forward, they are showing promise of being critical in unlocking several other opportunities, including combating the spread of fake content in social networks, detecting and preventing fraudulent online transactions in a timely fashion, and in ensuring collision avoidance in autonomous vehicle navigation, to name a few. Unfortunately, all these applications require more computational power than what can be provided by conventional computing systems. The key reason is that graph applications present large working sets that fail to fit in the small on-chip storage of existing computing systems, while at the same time they access data in seemingly unpredictable patterns, thus cannot draw benefit from traditional on-chip storage. In this dissertation, we set out to address the performance limitations of existing computing systems so to enable emerging graph applications like those described above. To achieve this, we identified three key strategies: 1) specializing memory architecture, 2) processing data near its storage, and 3) message coalescing in the network. Based on these strategies, this dissertation develops several solutions: OMEGA, which employs specialized on-chip storage units, with co-located specialized compute engines to accelerate the computation; MessageFusion, which coalesces messages in the interconnect; and Centaur, providing an architecture that optimizes the processing of infrequently-accessed data. Overall, these solutions provide 2x in performance improvements, with negligible hardware overheads, across a wide range of applications. Finally, we demonstrate the applicability of our strategies to other data-intensive domains, by exploring an acceleration solution for MapReduce applications, which achieves a 4x performance speedup, also with negligible area and power overheads.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163186/1/abrahad_1.pd

Cppless: Productive and Performant Serverless Programming in C++

The rise of serverless introduced a new class of scalable, elastic and highly available parallel workers in the cloud. Many systems and applications benefit from offloading computations and parallel tasks to dynamically allocated resources. However, the developers of C++ applications found it difficult to integrate functions due to complex deployment, lack of compatibility between client and cloud environments, and loosely typed input and output data. To enable single-source and efficient serverless acceleration in C++, we introduce Cppless, an end-to-end framework for implementing serverless functions which handles the creation, deployment, and invocation of functions. Cppless is built on top of LLVM and requires only two compiler extensions to automatically extract C++ function objects and deploy them to the cloud. We demonstrate that offloading parallel computations from a C++ application to serverless workers can provide up to 30x speedup, requiring only minor code modifications and costing less than one cent per computation

Elastic phone : towards detecting and mitigating computation and energy inefficiencies in mobile apps

Mobile devices have become ubiquitous and their ever evolving capabilities are bringing them closer to personal computers. Nonetheless, due to their mobility and small size factor constraints, they still present many hardware and software challenges. Their limited battery life time has led to the design of mobile networks that are inherently different from previous networks (e.g., wifi) and more restrictive task scheduling. Additionally, mobile device ecosystems are more susceptible to the heterogeneity of hardware and from conflicting interests of distributors, internet service providers, manufacturers, developers, etc. The high number of stakeholders ultimately responsible for the performance of a device, results in an inconsistent behavior and makes it very challenging to build a solution that improves resource usage in most cases. The focus of this thesis is on the study and development of techniques to detect and mitigate computation and energy inefficiencies in mobile apps. It follows a bottom-up approach, starting from the challenges behind detecting inefficient execution scheduling by looking only at apps’ implementations. It shows that scheduling APIs are largely misused and have a great impact on devices wake up frequency and on the efficiency of existing energy saving techniques (e.g., batching scheduled executions). Then it addresses many challenges of app testing in the dynamic analysis field. More specifically, how to scale mobile app testing with realistic user input and how to analyze closed source apps’ code at runtime, showing that introducing humans in the app testing loop improves the coverage of app’s code and generated network volume. Finally, using the combined knowledge of static and dynamic analysis, it focuses on the challenges of identifying the resource hungry sections of apps and how to improve their execution via offloading. There is a special focus on performing non-intrusive offloading transparent to existing apps and on in-network computation offloading and distribution. It shows that, even without a custom OS or app modifications, in-network offloading is still possible, greatly improving execution times, energy consumption and reducing both end-user experienced latency and request drop rates. It concludes with a real app measurement study, showing that a good portion of the most popular apps’ code can indeed be offloaded and proposes future directions for the app testing and computation offloading fields.Los dispositivos móviles se han tornado omnipresentes y sus capacidades están en constante evolución acercándolos a los computadoras personales. Sin embargo, debido a su movilidad y tamaño reducido, todavía presentan muchos desafíos de hardware y software. Su duración limitada de batería ha llevado al diseño de redes móviles que son inherentemente diferentes de las redes anteriores y una programación de tareas más restrictiva. Además, los ecosistemas de dispositivos móviles son más susceptibles a la heterogeneidad de hardware y los intereses conflictivos de las entidades responsables por el rendimiento final de un dispositivo. El objetivo de esta tesis es el estudio y desarrollo de técnicas para detectar y mitigar las ineficiencias de computación y energéticas en las aplicaciones móviles. Empieza con los desafíos detrás de la detección de planificación de ejecución ineficientes, mirando sólo la implementación de las aplicaciones. Se muestra que las API de planificación son en gran medida mal utilizadas y tienen un gran impacto en la frecuencia con que los dispositivos despiertan y en la eficiencia de las técnicas de ahorro de energía existentes. A continuación, aborda muchos desafíos de las pruebas de aplicaciones en el campo de análisis dinámica. Más específicamente, cómo escalar las pruebas de aplicaciones móviles con una interacción realista y cómo analizar código de aplicaciones de código cerrado durante la ejecución, mostrando que la introducción de humanos en el bucle de prueba de aplicaciones mejora la cobertura del código y el volumen de comunicación de red generado. Por último, combinando la análisis estática y dinámica, se centra en los desafíos de identificar las secciones de aplicaciones con uso intensivo de recursos y cómo mejorar su ejecución a través de la ejecución remota (i.e.,"offload"). Hay un enfoque especial en el "offload" no intrusivo y transparente a las aplicaciones existentes y en el "offload"y distribución de computación dentro de la red. Demuestra que, incluso sin un sistema operativo personalizado o modificaciones en la aplicación, el "offload" en red sigue siendo posible, mejorando los tiempos de ejecución, el consumo de energía y reduciendo la latencia del usuario final y las tasas de caída de solicitudes de "offload". Concluye con un estudio real de las aplicaciones más populares, mostrando que una buena parte de su código puede de hecho ser ejecutado remotamente y propone direcciones futuras para los campos de "offload" de aplicaciones.Postprint (published version

Elastic phone : towards detecting and mitigating computation and energy inefficiencies in mobile apps

Mobile devices have become ubiquitous and their ever evolving capabilities are bringing them closer to personal computers. Nonetheless, due to their mobility and small size factor constraints, they still present many hardware and software challenges. Their limited battery life time has led to the design of mobile networks that are inherently different from previous networks (e.g., wifi) and more restrictive task scheduling. Additionally, mobile device ecosystems are more susceptible to the heterogeneity of hardware and from conflicting interests of distributors, internet service providers, manufacturers, developers, etc. The high number of stakeholders ultimately responsible for the performance of a device, results in an inconsistent behavior and makes it very challenging to build a solution that improves resource usage in most cases. The focus of this thesis is on the study and development of techniques to detect and mitigate computation and energy inefficiencies in mobile apps. It follows a bottom-up approach, starting from the challenges behind detecting inefficient execution scheduling by looking only at apps’ implementations. It shows that scheduling APIs are largely misused and have a great impact on devices wake up frequency and on the efficiency of existing energy saving techniques (e.g., batching scheduled executions). Then it addresses many challenges of app testing in the dynamic analysis field. More specifically, how to scale mobile app testing with realistic user input and how to analyze closed source apps’ code at runtime, showing that introducing humans in the app testing loop improves the coverage of app’s code and generated network volume. Finally, using the combined knowledge of static and dynamic analysis, it focuses on the challenges of identifying the resource hungry sections of apps and how to improve their execution via offloading. There is a special focus on performing non-intrusive offloading transparent to existing apps and on in-network computation offloading and distribution. It shows that, even without a custom OS or app modifications, in-network offloading is still possible, greatly improving execution times, energy consumption and reducing both end-user experienced latency and request drop rates. It concludes with a real app measurement study, showing that a good portion of the most popular apps’ code can indeed be offloaded and proposes future directions for the app testing and computation offloading fields.Los dispositivos móviles se han tornado omnipresentes y sus capacidades están en constante evolución acercándolos a los computadoras personales. Sin embargo, debido a su movilidad y tamaño reducido, todavía presentan muchos desafíos de hardware y software. Su duración limitada de batería ha llevado al diseño de redes móviles que son inherentemente diferentes de las redes anteriores y una programación de tareas más restrictiva. Además, los ecosistemas de dispositivos móviles son más susceptibles a la heterogeneidad de hardware y los intereses conflictivos de las entidades responsables por el rendimiento final de un dispositivo. El objetivo de esta tesis es el estudio y desarrollo de técnicas para detectar y mitigar las ineficiencias de computación y energéticas en las aplicaciones móviles. Empieza con los desafíos detrás de la detección de planificación de ejecución ineficientes, mirando sólo la implementación de las aplicaciones. Se muestra que las API de planificación son en gran medida mal utilizadas y tienen un gran impacto en la frecuencia con que los dispositivos despiertan y en la eficiencia de las técnicas de ahorro de energía existentes. A continuación, aborda muchos desafíos de las pruebas de aplicaciones en el campo de análisis dinámica. Más específicamente, cómo escalar las pruebas de aplicaciones móviles con una interacción realista y cómo analizar código de aplicaciones de código cerrado durante la ejecución, mostrando que la introducción de humanos en el bucle de prueba de aplicaciones mejora la cobertura del código y el volumen de comunicación de red generado. Por último, combinando la análisis estática y dinámica, se centra en los desafíos de identificar las secciones de aplicaciones con uso intensivo de recursos y cómo mejorar su ejecución a través de la ejecución remota (i.e.,"offload"). Hay un enfoque especial en el "offload" no intrusivo y transparente a las aplicaciones existentes y en el "offload"y distribución de computación dentro de la red. Demuestra que, incluso sin un sistema operativo personalizado o modificaciones en la aplicación, el "offload" en red sigue siendo posible, mejorando los tiempos de ejecución, el consumo de energía y reduciendo la latencia del usuario final y las tasas de caída de solicitudes de "offload". Concluye con un estudio real de las aplicaciones más populares, mostrando que una buena parte de su código puede de hecho ser ejecutado remotamente y propone direcciones futuras para los campos de "offload" de aplicaciones

Elastic provisioning of network and computing resources at the edge for IoT services

The fast growth of Internet-connected embedded devices demands new system capabilities at the network edge, such as provisioning local data services on both limited network and computational resources. The current contribution addresses the previous problem by enhancing the usage of scarce edge resources. It designs, deploys, and tests a new solution that incorporates the positive functional advantages offered by software-defined networking (SDN), network function virtual-ization (NFV), and fog computing (FC). Our proposal autonomously activates or deactivates embedded virtualized resources, in response to clients’ requests for edge services. Complementing existing literature, the obtained results from extensive tests on our programmable proposal show the superior performance of the proposed elastic edge resource provisioning algorithm, which also assumes a SDN controller with proactive OpenFlow behavior. According to our results, the maximum flow rate for the proactive controller is 15% higher; the maximum delay is 83% smaller; and the loss is 20% smaller compared to when the non-proactive controller is in operation. This improvement in flow quality is complemented by a reduction in control channel workload. The controller also records the time duration of each edge service session, which can enable the ac-counting of used resources per session.info:eu-repo/semantics/publishedVersio