Diluting the Scalability Boundaries: Exploring the Use of Disaggregated Architectures for High-Level Network Data Analysis

Traditional data centers are designed with a rigid architecture of fit-for-purpose servers that provision resources beyond the average workload in order to deal with occasional peaks of data. Heterogeneous data centers are pushing towards more cost-efficient architectures with better resource provisioning. In this paper we study the feasibility of using disaggregated architectures for intensive data applications, in contrast to the monolithic approach of server-oriented architectures. Particularly, we have tested a proactive network analysis system in which the workload demands are highly variable. In the context of the dReDBox disaggregated architecture, the results show that the overhead caused by using remote memory resources is significant, between 66\% and 80\%, but we have also observed that the memory usage is one order of magnitude higher for the stress case with respect to average workloads. Therefore, dimensioning memory for the worst case in conventional systems will result in a notable waste of resources. Finally, we found that, for the selected use case, parallelism is limited by memory. Therefore, using a disaggregated architecture will allow for increased parallelism, which, at the same time, will mitigate the overhead caused by remote memory.Comment: 8 pages, 6 figures, 2 tables, 32 references. Pre-print. The paper will be presented during the IEEE International Conference on High Performance Computing and Communications in Bangkok, Thailand. 18 - 20 December, 2017. To be published in the conference proceeding

A Convolve-And-MErge Approach for Exact Computations on High-Performance Reconfigurable Computers

This work presents an approach for accelerating arbitrary-precision arithmetic on high-performance reconfigurable computers (HPRCs). Although faster and smaller, fixed-precision arithmetic has inherent rounding and overflow problems that can cause errors in scientific or engineering applications. This recurring phenomenon is usually referred to as numerical nonrobustness. Therefore, there is an increasing interest in the paradigm of exact computation, based on arbitrary-precision arithmetic. There are a number of libraries and/or languages supporting this paradigm, for example, the GNU multiprecision (GMP) library. However, the performance of computations is significantly reduced in comparison to that of fixed-precision arithmetic. In order to reduce this performance gap, this paper investigates the acceleration of arbitrary-precision arithmetic on HPRCs. A Convolve-And-MErge approach is proposed, that implements virtual convolution schedules derived from the formal representation of the arbitrary-precision multiplication problem. Additionally, dynamic (nonlinear) pipeline techniques are also exploited in order to achieve speedups ranging from 5x (addition) to 9x (multiplication), while keeping resource usage of the reconfigurable device low, ranging from 11% to 19%

A PCIe DMA engine to support the virtualization of 40 Gbps FPGA-accelerated network appliances

Network Function Virtualization (NFV) allows creating specialized network appliances out of general-purpose computing equipment (servers, storage, and switches). In this paper we present a PCIe DMA engine that allows boosting the performance of virtual network appliances by using FPGA accelerators. Two key technologies are demonstrated, SR-IOV and PCI Passthrough. Using these two technologies, a single FPGA board can accelerate several virtual software appliances. The final goal is, in an NFV scenario, to substitute conventional Ethernet NICs by networking FPGA boards (such as NetFPGA SUME). The advantage of this approach is that FPGAs can very efficiently implement many networking tasks, thus boosting the performance of virtual networking appliances. The SR-IOV capable PCIe DMA engine presented in this work, as well as its associated driver, are key elements in achieving this goal of using FPGA networking boards instead of conventional NICs. Both DMA engine and driver are open source, and target the Xilinx 7-Series and UltraScale PCIe Gen3 endpoint. The design has been tested on a NetFPGA SUME board, offering transfer rates reaching 50 Gb/s for bulk transmissions. By taking advantage of SR-IOV and PCI Passthrough technologies, our DMA engine provides transfers rate well above 40 Gb/s for data transmissions from the FPGA to a virtual machine. We have also identified the bottlenecks in the use of virtualized FPGA accelerators caused by reductions in the maximum read request size and maximum payload PCIe parameters. Finally, the DMA engine presented in this paper is a very compact design, using just 2% of a Xilinx Virtex-7 XC7VX690T device.This work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project PackTrack (TEC2012-33754) and by the European Union through the Integrated Project (IP) IDEALIST under grant agreement FP7- 317999. The stay of Sergio Lopez-Buedo at the University of Cambridge was funded by the Spanish Government under a ”Jose Castillejo” grant. Additionally, this research was sponsored by EU Horizon 2020 SSICLOPS (agreement No. 644866) research program and EPSRC through Networks as a Service (NaaS) (EP/K034723/1) project.This is the author accepted manuscript. The final version is available from IEEE at http://dx.doi.org/10.1109/ReConFig.2015.7393334

Thermal analysis and modeling of embedded processors

This paper presents a complete modeling approach to analyze the thermal behavior of microprocessor-based systems. While most compact modeling approaches require a deep knowledge of the implementation details, our method defines a black box technique which can be applied to different target processors when this detailed information is unknown. The obtained results show high accuracy, applicability and can be easily automated. The proposed methodology has been used to study the impact of code transformations in the thermal behavior of the chip. Finally, the analysis of the thermal effect of the source code modifications can be included in a temperature-aware compiler which minimizes the total temperature of the chip, as well as the temperature gradients, according to these guidelines

dReDBox: Materializing a full-stack rack-scale system prototype of a next-generation disaggregated datacenter

Current datacenters are based on server machines, whose mainboard and hardware components form the baseline, monolithic building block that the rest of the system software, middleware and application stack are built upon. This leads to the following limitations: (a) resource proportionality of a multi-tray system is bounded by the basic building block (mainboard), (b) resource allocation to processes or virtual machines (VMs) is bounded by the available resources within the boundary of the mainboard, leading to spare resource fragmentation and inefficiencies, and (c) upgrades must be applied to each and every server even when only a specific component needs to be upgraded. The dRedBox project (Disaggregated Recursive Datacentre-in-a-Box) addresses the above limitations, and proposes the next generation, low-power, across form-factor datacenters, departing from the paradigm of the mainboard-as-a-unit and enabling the creation of function-block-as-a-unit. Hardware-level disaggregation and software-defined wiring of resources is supported by a full-fledged Type-1 hypervisor that can execute commodity virtual machines, which communicate over a low-latency and high-throughput software-defined optical network. To evaluate its novel approach, dRedBox will demonstrate application execution in the domains of network functions virtualization, infrastructure analytics, and real-time video surveillance.This work has been supported in part by EU H2020 ICTproject dRedBox, contract #687632.Peer ReviewedPostprint (author's final draft

On interconnecting and orchestrating components in disaggregated data centers:The dReDBox project vision

Computing systems servers-low-or high-end ones have been traditionally designed and built using a main-board and its hardware components as a 'hard' monolithic building block; this formed the base unit on which the system hardware and software stack design build upon. This hard deployment and management border on compute, memory, network and storage resources is either fixed or quite limited in expandability during design time and in practice remains so throughout machine lifetime as subsystem upgrades are seldomely employed. The impact of this rigidity has well known ramifications in terms of lower system resource utilization, costly upgrade cycles and degraded energy proportionality. In the dReDBox project we take on the challenge of breaking the server boundaries through materialization of the concept of disaggregation. The basic idea of the dReDBox architecture is to use a core of high-speed, low-latency opto-electronic fabric that will bring physically distant components more closely in terms of latency and bandwidth. We envision a powerful software-defined control plane that will match the flexibility of the system to the resource needs of the applications (or VMs) running in the system. Together the hardware, interconnect, and software architectures will enable the creation of a modular, vertically-integrated system that will form a datacenter-in-a-box