FLASH 1.0: A Software Framework for Rapid Parallel Deployment and Enhancing Host Code Portability in Heterogeneous Computing

In this paper, we present FLASH 1.0, a C++-based software framework for rapid parallel deployment and enhancing host code portability in heterogeneous computing. FLASH takes a novel approach in describing kernels and dynamically dispatching them in a hardware-agnostic manner. FLASH features truly hardware-agnostic frontend interfaces, which not only unify the compile-time control flow but also enforces a portability-optimized code organization that imposes a demarcation between computational (performance-critical) and functional (non-performance-critical) codes as well as the separation of hardware-specific and hardware-agnostic codes in the host application. We use static code analysis to measure the hardware independence ratio of popular HPC applications and show that up to 99.72% code portability can be achieved with FLASH. Similarly, we measure the complexity of state-of-the-art portable programming models and show that a code reduction of up to 2.2x can be achieved for two common HPC kernels while maintaining 100% code portability with a normalized framework overhead between 1% - 13% of the total kernel runtime. The codes are available at https://github.com/PSCLab-ASU/FLASH.Comment: 12 page

Analysis and Approximation of Optimal Co-Scheduling on CMP

In recent years, the increasing design complexity and the problems of power and heat dissipation have caused a shift in processor technology to favor Chip Multiprocessors. In Chip Multiprocessors (CMP) architecture, it is common that multiple cores share some on-chip cache. The sharing may cause cache thrashing and contention among co-running jobs. Job co-scheduling is an approach to tackling the problem by assigning jobs to cores appropriately so that the contention and consequent performance degradations are minimized. This dissertation aims to tackle two of the most prominent challenges in job co-scheduling.;The first challenge is in the computational complexity for determining optimal job co-schedules. This dissertation presents one of the first systematic analyses on the complexity of job co-scheduling. Besides proving the NP completeness of job co-scheduling, it introduces a set of algorithms, based on graph theory and Integer/Linear Programming, for computing optimal co-schedules or their lower bounds in scenarios with or without job migrations. For complex cases, it empirically demonstrates the feasibility for approximating the optimal schedules effectively by proposing several heuristics-based algorithms. These discoveries facilitate the assessment of job co-schedulers by providing necessary baselines, and shed insights to the development of practical co-scheduling systems.;The second challenge resides in the prediction of the performance of processes co-running on a shared cache. This dissertation explores the influence on co-run performance prediction imposed by co-runners, program inputs, and cache configurations. Through a sequence of formal analysis, we derive an analytical co-run locality model, uncovering the inherent statistical connections between the data references of programs single-runs and their co-run locality. The model offers theoretical insights on co-run locality analysis and leads to a lightweight approach for fast prediction of shared cache performance. We demonstrate the effectiveness of the model in enabling proactive job co-scheduling.;Together, the two-dimensional findings open up many new opportunities for cache management on modern CMP by laying the foundation for job co-scheduling, and enhancing the understanding to data locality and cache sharing significantly

Stellar Mergers with HPX-Kokkos and SYCL: Methods of using an Asynchronous Many-Task Runtime System with SYCL

Ranging from NVIDIA GPUs to AMD GPUs and Intel GPUs: Given the heterogeneity of available accelerator cards within current supercomputers, portability is a key aspect for modern HPC applications. In Octo-Tiger, we rely on Kokkos and its various execution spaces for portable compute kernels. In turn, we use HPX to coordinate kernel launches, CPU tasks, and communication. This combination allows us to have a fine interleaving between portable CPU/GPU computations and communication, enabling scalability on various supercomputers. However, for HPX and Kokkos to work together optimally, we need to be able to treat Kokkos kernels as HPX tasks. Otherwise, instead of integrating asynchronous Kokkos kernel launches into HPX's task graph, we would have to actively wait for them with fence commands, which wastes CPU time better spent otherwise. Using an integration layer called HPX-Kokkos, treating Kokkos kernels as tasks already works for some Kokkos execution spaces (like the CUDA one), but not for others (like the SYCL one). In this work, we started making Octo-Tiger and HPX itself compatible with SYCL. To do so, we introduce numerous software changes, most notably an HPX-SYCL integration. This integration allows us to treat SYCL events as HPX tasks, which in turn allows us to better integrate Kokkos by extending the support of HPX-Kokkos to also fully support Kokkos' SYCL execution space. We show two ways to implement this HPX-SYCL integration and test them using Octo-Tiger and its Kokkos kernels, on both an NVIDIA A100 and an AMD MI100. We find modest, yet noticeable, speedups by enabling this integration, even when just running simple single-node scenarios with Octo-Tiger where communication and CPU utilization are not yet an issue

HALO 1.0: A Hardware-agnostic Accelerator Orchestration Framework for Enabling Hardware-agnostic Programming with True Performance Portability for Heterogeneous HPC

This paper presents HALO 1.0, an open-ended extensible multi-agent software framework that implements a set of proposed hardware-agnostic accelerator orchestration (HALO) principles. HALO implements a novel compute-centric message passing interface (C^2MPI) specification for enabling the performance-portable execution of a hardware-agnostic host application across heterogeneous accelerators. The experiment results of evaluating eight widely used HPC subroutines based on Intel Xeon E5-2620 CPUs, Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows for a unified control flow for host programs to run across all the computing devices with a consistently top performance portability score, which is up to five orders of magnitude higher than the OpenCL-based solution.Comment: 21 page

DECK: A new model for a distributed executive kernel integrating communication and multithreading for support of distributed object oriented application with fault tolerance support

DECK (Distributed Executive Communication Kernel) is a communication layer that provides support for multithreading and fault tolerance support. The approach retained in DECK is close to other distributed communication kernels like PM2, Athapascan, Nexus, TPVM or Chant in its way to integrate communication and multithreading to efficiently overlap communication by computation and provide low latency remote thread creation mechanisms. However, DECK differs from these communication kernels from the services offered and its modular architecture. The main goal of DECK is to implement a new model for the design of distributed executive kernel to efficiently use the new underlying hardware architectures (SMP architectures and fast communication adapters like Myrinet or memory oriented adapter like SCI) and provide a portable layer that abstract the problems linked with the integration of communication and multithreading while offering support for heterogeneity. A great lack in the current implementation of communication libraries or distributed executive kernel is the support for basic services at the thread level and support for fault tolerance support. Indeed, communication library like PVM or MPI are often used as communication layer to ensure portability and take benefits of specific implementation to ensure a good efficiency on specific architectures however the support for fault tolerance support, multithreading, scalability and interoperability are usually not offered. In the case of DECK, we propose a model where a distributed application can dynamically instantiate clusters of processes among an heterogeneous network of computers or parallel machines and this using multiple communication protocols or communication interfaces to ensure good performances regarding the underlying hardware architecture. The programming model proposed offer both classic synchronous and asynchronous remote service calls for thread creation and message passing for synchronization and data exchange. These basic functionalities, that form the low level communication and execution layer of DECK, are enforced by a service layer that propose the basic fault tolerant services like naming and group services or data management services for the marshaling and un-marshalling of complex data structures. The layered and modular approach followed by DECK enable many other extensions while keeping a high degree of portability and efficiency.Sistemas Distribuidos - Redes ConcurrenciaRed de Universidades con Carreras en Informática (RedUNCI

Advanced analytics through FPGA based query processing and deep reinforcement learning

Today, vast streams of structured and unstructured data have been incorporated in databases, and analytical processes are applied to discover patterns, correlations, trends and other useful relationships that help to take part in a broad range of decision-making processes. The amount of generated data has grown very large over the years, and conventional database processing methods from previous generations have not been sufficient to provide satisfactory results regarding analytics performance and prediction accuracy metrics. Thus, new methods are needed in a wide array of fields from computer architectures, storage systems, network design to statistics and physics. This thesis proposes two methods to address the current challenges and meet the future demands of advanced analytics. First, we present AxleDB, a Field Programmable Gate Array based query processing system which constitutes the frontend of an advanced analytics system. AxleDB melds highly-efficient accelerators with memory, storage and provides a unified programmable environment. AxleDB is capable of offloading complex Structured Query Language queries from host CPU. The experiments have shown that running a set of TPC-H queries, AxleDB can perform full queries between 1.8x and 34.2x faster and 2.8x to 62.1x more energy efficient compared to MonetDB, and PostgreSQL on a single workstation node. Second, we introduce TauRieL, a novel deep reinforcement learning (DRL) based method for combinatorial problems. The design idea behind combining DRL and combinatorial problems is to apply the prediction capabilities of deep reinforcement learning and to use the universality of combinatorial optimization problems to explore general purpose predictive methods. TauRieL utilizes an actor-critic inspired DRL architecture that adopts ordinary feedforward nets. Furthermore, TauRieL performs online training which unifies training and state space exploration. The experiments show that TauRieL can generate solutions two orders of magnitude faster and performs within 3% of accuracy compared to the state-of-the-art DRL on the Traveling Salesman Problem while searching for the shortest tour. Also, we present that TauRieL can be adapted to the Knapsack combinatorial problem. With a very minimal problem specific modification, TauRieL can outperform a Knapsack specific greedy heuristics.Hoy en día, se han incorporado grandes cantidades de datos estructurados y no estructurados en las bases de datos, y se les aplican procesos analíticos para descubrir patrones, correlaciones, tendencias y otras relaciones útiles que se utilizan mayormente para la toma de decisiones. La cantidad de datos generados ha crecido enormemente a lo largo de los años, y los métodos de procesamiento de bases de datos convencionales utilizados en las generaciones anteriores no son suficientes para proporcionar resultados satisfactorios respecto al rendimiento del análisis y respecto de la precisión de las predicciones. Por lo tanto, se necesitan nuevos métodos en una amplia gama de campos, desde arquitecturas de computadoras, sistemas de almacenamiento, diseño de redes hasta estadísticas y física. Esta tesis propone dos métodos para abordar los desafíos actuales y satisfacer las demandas futuras de análisis avanzado. Primero, presentamos AxleDB, un sistema de procesamiento de consultas basado en FPGAs (Field Programmable Gate Array) que constituye la interfaz de un sistema de análisis avanzado. AxleDB combina aceleradores altamente eficientes con memoria, almacenamiento y proporciona un entorno programable unificado. AxleDB es capaz de descargar consultas complejas de lenguaje de consulta estructurado desde la CPU del host. Los experimentos han demostrado que al ejecutar un conjunto de consultas TPC-H, AxleDB puede realizar consultas completas entre 1.8x y 34.2x más rápido y 2.8x a 62.1x más eficiente energéticamente que MonetDB, y PostgreSQL en un solo nodo de una estación de trabajo. En segundo lugar, presentamos TauRieL, un nuevo método basado en Deep Reinforcement Learning (DRL) para problemas combinatorios. La idea central que está detrás de la combinación de DRL y problemas combinatorios, es aplicar las capacidades de predicción del aprendizaje de refuerzo profundo y el uso de la universalidad de los problemas de optimización combinatoria para explorar métodos predictivos de propósito general. TauRieL utiliza una arquitectura DRL inspirada en el actor-crítico que se adapta a redes feedforward. Además, TauRieL realiza el entrenamieton en línea que unifica el entrenamiento y la exploración espacial de los estados. Los experimentos muestran que TauRieL puede generar soluciones dos órdenes de magnitud más rápido y funciona con un 3% de precisión en comparación con el estado del arte en DRL aplicado al problema del viajante mientras busca el recorrido más corto. Además, presentamos que TauRieL puede adaptarse al problema de la Mochila. Con una modificación específica muy mínima del problema, TauRieL puede superar a una heurística codiciosa de Knapsack Problem.Postprint (published version

A tilted grating interferometer for full vector field differential x-ray phase contrast tomography

We report on a setup for differential x-ray phase-contrast imaging and tomography, that measures the full 2D phase-gradient information. The setup uses a simple one-dimensional x-ray grating interferometer, in which the grating structures of the interferometer are oriented at a tilt angle with respect to the sample rotation axis. In such a configuration, the differential phase images from opposing tomography projections can be combined to yield both components of the gradient vector. We show how the refractive index distribution as well as its x, y, and z gradient components can be reconstructed directly from the recorded projection data. The method can equally well be applied at conventional x-ray tube sources, to analyzer based x-ray imaging or neutron imaging. It is demonstrated with measurements of an x-ray phantom and a rat brain using synchrotron radiation

The Road to Next-Generation Multiple Access: A 50-Year Tutorial Review

The evolution of wireless communications has been significantly influenced by remarkable advancements in multiple access (MA) technologies over the past five decades, shaping the landscape of modern connectivity. Within this context, a comprehensive tutorial review is presented, focusing on representative MA techniques developed over the past 50 years. The following areas are explored: i) The foundational principles and information-theoretic capacity limits of power-domain non-orthogonal multiple access (NOMA) are characterized, along with its extension to multiple-input multiple-output (MIMO)-NOMA. ii) Several MA transmission schemes exploiting the spatial domain are investigated, encompassing both conventional space-division multiple access (SDMA)/MIMO-NOMA systems and near-field MA systems utilizing spherical-wave propagation models. iii) The application of NOMA to integrated sensing and communications (ISAC) systems is studied. This includes an introduction to typical NOMA-based downlink/uplink ISAC frameworks, followed by an evaluation of their performance limits using a mutual information (MI)-based analytical framework. iv) Major issues and research opportunities associated with the integration of MA with other emerging technologies are identified to facilitate MA in next-generation networks, i.e., next-generation multiple access (NGMA). Throughout the paper, promising directions are highlighted to inspire future research endeavors in the realm of MA and NGMA.Comment: 43 pages, 38 figures; Submitted to Proceedings of the IEE