Modern big data workflows are characterized by computationally intensive kernels. The simulated results are often combined with knowledge extracted from AI models to ultimately support decision-making. These energy-hungry workflows are increasingly executed in data centers with energy-efficient hard-ware accelerators since FPG As are well-suited for this task due to their inherent parallelism. We present the H2020 project EVEREST, which has developed a system development kit (SDK) to simplify the creation of FPGA-accelerated kernels and manage the execution at runtime through a virtualization environment. This paper describes the main components of the EVEREST SDK and the benefits that can be achieved in our use cases.

Over the years, Field Programmable Gate Arrays (FPGA) have been gaining popularity in the High Performance Computing (HPC) field, because their reconfigurability enables very fine-grained optimizations with low energy cost. However, the different characteristics, architectures, and network topologies of the clusters have hindered the use of FPGAs at a large scale. In this work, we present an evolution of OmpSs@FPGA, a high-level task-based programming model and extension to OmpSs-2, that aims at unifying all FPGA clusters by using a message-passing interface that is compatible with FPGA accelerators. These accelerators are programmed with C/C++ pragmas, and synthesized with High-Level Synthesis tools. The new framework includes a custom protocol to exchange messages between FPGAs, agnostic of the architecture and network type. On top of that, we present a new communication paradigm called Implicit Message Passing (IMP), where the user does not need to call any message-passing API. Instead, the runtime automatically infers data movement between nodes. We test classic message passing and IMP with three benchmarks on two different FPGA clusters. One is cloudFPGA, a disaggregated platform with AMD FPGAs that are only connected to the network through UDP/TCP/IP. The other is ESSPER, composed of CPU-attached Intel FPGAs that have a private network at the ethernet level. In both cases, we demonstrate that IMP with OmpSs@FPGA can increase the productivity of FPGA programmers at a large scale thanks to simplifying communication between nodes, without limiting the scalability of applications. We implement the N-body, Heat simulation and Cholesky decomposition benchmarks, and show that FPGA clusters get 2.6x and 2.4x better performance per watt than a CPU-only supercomputer for N-body and Heat.

Immersive and interactive experiences offered by virtual world simulations (VWS) are becoming increasingly indispensable in today's digital era, particularly in domains such as gaming, engineering, and education. Cloud computing provides a compelling solution for VWS, with its scalability, costeffectiveness, and accessibility, combined with efficient processing capabilities of hardware accelerators such as FPGAs and GPUs. This paper presents APEIRON, an in-progress distributed library built on the Ray framework, that aims to facilitate the effective composition of hardware accelerators on public clouds and emerging disaggregated platforms. Additionally, this work introduces a novel composability metric, analyzing the intricate relationship between resource utilization and workload performance. This study focuses on theoretical composability analyses, laying groundwork for optimized utilization of hardware resources to enhance efficiency, thereby potentially reducing operational costs.

Nowadays, a new parallel paradigm for energy-efficient heterogeneous hardware infrastructures is required to achieve better performance at a reasonable cost on high-performance computing applications. Under this new paradigm, some application parts are offloaded to specialized accelerators that run faster or are more energy-efficient than CPUs. Field-Programmable Gate Arrays (FPGA) are one of those types of accelerators that are becoming widely available in data centers. This paper proposes OmpSs@cloudFPGA, which includes novel extensions to parallel task-based programming models that enable easy and efficient programming of heterogeneous clusters with FPGAs. The programmer only needs to annotate, with OpenMP-like pragmas, the tasks of the application that should be accelerated in the cluster of FPGAs. Next, the proposed programming model framework automatically extracts parts annotated with High-Level Synthesis (HLS) pragmas and synthesizes them into hardware accelerator cores for FPGAs. Additionally, our extensions include and support two novel features: 1) FPGA-to-FPGA direct communication using a Message Passing Interface (MPI) similar Application Programming Interface (API) with one-to-one and collective communications to alleviate host communication channel bottleneck, and 2) creating and spawning work from inside the FPGAs to their own accelerator cores based on an MPI rank-like identification. These features break the classical host-accelerator model, where the host (typically the CPU) generates all the work and distributes it to each accelerator. We also present an evaluation of OmpSs@cloudFPGA for different parallel strategies of the N-Body application on the IBM cloudFPGA research platform. Results show that for cluster sizes up to 56 FPGAs, the performance scales linearly. To the best of our knowledge, this is the best performance obtained for N-body over FPGA platforms, reaching 344 Gpairs/s with 56 FPGAs. Finally, we compare the performance and power consumption of the proposed approach with the ones obtained by a classical execution on the MareNostrum 4 supercomputer, demonstrating that our FPGA approach reduces power consumption by an order of magnitude.

In this article, we survey existing academic and commercial efforts to provide Field-Programmable Gate Array (FPGA) acceleration in datacenters and the cloud. The goal is a critical review of existing systems and a discussion of their evolution from single workstations with PCI-attached FPGAs in the early days of reconfigurable computing to the integration of FPGA farms in large-scale computing infrastructures. From the lessons learned, we discuss the future of FPGAs in datacenters and the cloud and assess the challenges likely to be encountered along the way. The article explores current architectures and discusses scalability and abstractions supported by operating systems, middleware, and virtualization. Hardware and software security becomes critical when infrastructure is shared among tenants with disparate backgrounds. We review the vulnerabilities of current systems and possible attack scenarios and discuss mitigation strategies, some of which impact FPGA architecture and technology. The viability of these architectures for popular applications is reviewed, with a particular focus on deep learning and scientific computing. This work draws from workshop discussions, panel sessions including the participation of experts in the reconfigurable computing field, and private discussions among these experts. These interactions have harmonized the terminology, taxonomy, and the important topics covered in this manuscript.

The evolution of cloud applications into loosely-coupled microservices opens new opportunities for hardware accelerators to improve workload performance. Existing accelerator techniques for cloud sacrifice the consolidation benefits of microservices. This paper presents CloudiFi, a framework to deploy and compare accelerators as a cloud service. We evaluate our framework in the context of a financial workload and present early results indicating up to 485 x gains in microservice response time.

The slowdown of Moore's law and the end of Dennard scaling created a demand for specialized accelerators, including Field Programmable Gate Arrays (FPGAs), in cloud data centers. At the same time, compute resources are increasingly consumed via public and private clouds and traditional applications are modernized using scalable microservices and Function-as-a-Service (FaaS) offerings. Nonetheless, true FaaS based on FPGAs or other accelerators is virtually absent from the offering catalogs of all major cloud providers. In addition, FPGA applications are typically coded in a monolithic fashion, due to device and vendor specific dependencies, which reduces the portability and usability of FPGA cloud offerings further. However, FPGA-based FaaS can improve execution efficiency and minimize (tail-) latencies while decreasing costs. We propose a novel system architecture, called Mantle, that uses disaggregated FPGAs to enable scalable, usable, portable and efficient FaaS offerings for FPGAs. Our experimental results demonstrate a significant reduction of end-to-end service provisioning time to below 7 seconds and an increase in execution efficiency by a factor of 4 with negligible overhead.

With Moore's law progressively running out of steam, heterogeneous computing architectures have been powering the top supercomputers in the world for many years and are now finding broader adoption across the industry. The trend towards sustainable computing also requires domain-specific heterogeneous hardware architectures, which promise further gains in energy efficiency. At the same time, today's high performance computing applications have evolved from monolithic simulations in a single domain to multidisciplinary complex workflows. In this paper, we explore how these trends affect system design decisions and what this means for future computing system architectures.

Over the past two decades, the Message Passing Interface (MPI) has evolved as the de-facto standard for programming High-Performance Computing (HPC) clusters. Its widespread utilization led to the rapid development of applications and high reusability. Meanwhile, energy- and compute-efficient devices such as Field-Programmable Gate Arrays (FPGAs) are stepping into modern data centers and HPC clusters to address the nearing end of technology scaling. This combination of traditional CPU servers and FPGA nodes leads to Reconfigurable Heterogeneous HPC (ReH2PC) systems that are particularly cumbersome to program because of the absence of a standard programming model. This work advocates the use of MPI to program such ReH2PC clusters and presents a proof of concept based on a cross-compiler, a High-Level Synthesis library, C++ library, an FPGA- and a CPU-runtime environment. The result is a one-click solution, which compiles a standard MPI application for a ReH2PC cluster.

Specialized accelerators for tensor-operations, such as blocked-matrix operations and multi-dimensional convolutions, have emerged as powerful architecture choices for high-performance Deep-Learning computing. The rapid development of frameworks, models, and precision options challenges the adaptability of such tensor-accelerators since the adaptation to new requirements incurs significant engineering costs. Programmable tensor accelerators offer a promising alternative by allowing reconfiguration of a virtual architecture that overlays on top of the physical FPGA configurable fabric. We propose an overlay (τ-VTA) and an optimization method guided by agile-inspired auto-tuning techniques. We achieve higher performance of up to 2.5x and faster convergence of up to 8.1x.

Emerging applications such as deep neural networks, bioinformatics or video encoding impose a high computing pressure on the Cloud. Reconfigurable technologies like Field-Programmable Gate Arrays (FPGAs) can handle such compute-intensive workloads in an efficient and performant way. To seamlessly incorporate FPGAs into existing Cloud environments and leverage their full power efficiency, FPGAs should be directly attached to the data center network and operate independent of power-hungry CPUs. This raises new questions about resource management, application deployment and network integrity. We present a system architecture for managing a large number of network-attached FPGAs in an efficient, flexible and scalable way. To ensure the integrity of the infrastructure, we use partial reconfiguration to separate the non-privileged user logic from the privileged system logic. To create a really scalable and agile cloud service, the management of all resources builds on the Representational State Transfer (REST) concept.