20 research outputs found
P4-compatible High-level Synthesis of Low Latency 100 Gb/s Streaming Packet Parsers in FPGAs
Packet parsing is a key step in SDN-aware devices. Packet parsers in SDN
networks need to be both reconfigurable and fast, to support the evolving
network protocols and the increasing multi-gigabit data rates. The combination
of packet processing languages with FPGAs seems to be the perfect match for
these requirements. In this work, we develop an open-source FPGA-based
configurable architecture for arbitrary packet parsing to be used in SDN
networks. We generate low latency and high-speed streaming packet parsers
directly from a packet processing program. Our architecture is pipelined and
entirely modeled using templated C++ classes. The pipeline layout is derived
from a parser graph that corresponds a P4 code after a series of graph
transformation rounds. The RTL code is generated from the C++ description using
Xilinx Vivado HLS and synthesized with Xilinx Vivado. Our architecture achieves
100 Gb/s data rate in a Xilinx Virtex-7 FPGA while reducing the latency by 45%
and the LUT usage by 40% compared to the state-of-the-art.Comment: Accepted for publication at the 26th ACM/SIGDA International
Symposium on Field-Programmable Gate Arrays February 25 - 27, 2018 Monterey
Marriott Hotel, Monterey, California, 7 pages, 7 figures, 1 tabl
Module-per-Object: a Human-Driven Methodology for C++-based High-Level Synthesis Design
High-Level Synthesis (HLS) brings FPGAs to audiences previously unfamiliar to
hardware design. However, achieving the highest Quality-of-Results (QoR) with
HLS is still unattainable for most programmers. This requires detailed
knowledge of FPGA architecture and hardware design in order to produce
FPGA-friendly codes. Moreover, these codes are normally in conflict with best
coding practices, which favor code reuse, modularity, and conciseness.
To overcome these limitations, we propose Module-per-Object (MpO), a
human-driven HLS design methodology intended for both hardware designers and
software developers with limited FPGA expertise. MpO exploits modern C++ to
raise the abstraction level while improving QoR, code readability and
modularity. To guide HLS designers, we present the five characteristics of MpO
classes. Each characteristic exploits the power of HLS-supported modern C++
features to build C++-based hardware modules. These characteristics lead to
high-quality software descriptions and efficient hardware generation. We also
present a use case of MpO, where we use C++ as the intermediate language for
FPGA-targeted code generation from P4, a packet processing domain specific
language. The MpO methodology is evaluated using three design experiments: a
packet parser, a flow-based traffic manager, and a digital up-converter. Based
on experiments, we show that MpO can be comparable to hand-written VHDL code
while keeping a high abstraction level, human-readable coding style and
modularity. Compared to traditional C-based HLS design, MpO leads to more
efficient circuit generation, both in terms of performance and resource
utilization. Also, the MpO approach notably improves software quality,
augmenting parametrization while eliminating the incidence of code duplication.Comment: 9 pages. Paper accepted for publication at The 27th IEEE
International Symposium on Field-Programmable Custom Computing Machines, San
Diego CA, April 28 - May 1, 201
Bridging the Gap: FPGAs as Programmable Switches
The emergence of P4, a domain specific language, coupled to PISA, a domain
specific architecture, is revolutionizing the networking field. P4 allows to
describe how packets are processed by a programmable data plane, spanning ASICs
and CPUs, implementing PISA. Because the processing flexibility can be limited
on ASICs, while the CPUs performance for networking tasks lag behind, recent
works have proposed to implement PISA on FPGAs. However, little effort has been
dedicated to analyze whether FPGAs are good candidates to implement PISA. In
this work, we take a step back and evaluate the micro-architecture efficiency
of various PISA blocks. We demonstrate, supported by a theoretical and
experimental analysis, that the performance of a few PISA blocks is severely
limited by the current FPGA architectures. Specifically, we show that match
tables and programmable packet schedulers represent the main performance
bottlenecks for FPGA-based programmable switches. Thus, we explore two avenues
to alleviate these shortcomings. First, we identify network applications well
tailored to current FPGAs. Second, to support a wider range of networking
applications, we propose modifications to the FPGA architectures which can also
be of interest out of the networking field.Comment: To be published in : IEEE International Conference on High
Performance Switching and Routing 202
Design Principles for Packet Deparsers on FPGAs
The P4 language has drastically changed the networking field as it allows to quickly describe and implement new networking applications. Although a large variety of applications can be described with the P4 language, current programmable switch architectures impose significant constraints on P4 programs. To address this shortcoming, FPGAs have been explored as potential targets for P4 applications. P4 applications are described using three abstractions: a packet parser, match-action tables, and a packet deparser, which reassembles the output packet with the result of the match-action tables. While implementations of packet parsers and match-action tables on FPGAs have been widely covered in the literature, no general design principles have been presented for the packet deparser. Indeed, implementing a high-speed and efficient deparser on FPGAs remains an open issue because it requires a large amount of interconnections and the architecture must be tailored to a P4 program. As a result, in several works where a P4 application is implemented on FPGAs, the deparser consumes a significant proportion of chip resources. Hence, in this paper, we address this issue by presenting design principles for efficient and high-speed deparsers on FPGAs. As an artifact, we introduce a tool that generates an efficient vendor-agnostic deparser architecture from a P4 program.Our design has been validated and simulated with a cocotb-based framework.The resulting architecture is implemented on Xilinx Ultrascale+ FPGAs and supports a throughput of more than 200 Gbps while reducing resource usage by almost 10x compared to other solutions
Polymorphisms in the MBL2 gene are associated with the plasma levels of MBL and the cytokines IL-6 and TNF-α in severe COVID-19
IntroductionMannose-binding lectin (MBL) promotes opsonization, favoring phagocytosis and activation of the complement system in response to different microorganisms, and may influence the synthesis of inflammatory cytokines. This study investigated the association of MBL2 gene polymorphisms with the plasma levels of MBL and inflammatory cytokines in COVID-19.MethodsBlood samples from 385 individuals (208 with acute COVID-19 and 117 post-COVID-19) were subjected to real-time PCR genotyping. Plasma measurements of MBL and cytokines were performed by enzyme-linked immunosorbent assay and flow cytometry, respectively.ResultsThe frequencies of the polymorphic MBL2 genotype (OO) and allele (O) were higher in patients with severe COVID-19 (p< 0.05). The polymorphic genotypes (AO and OO) were associated with lower MBL levels (p< 0.05). IL-6 and TNF-α were higher in patients with low MBL and severe COVID-19 (p< 0.05). No association of polymorphisms, MBL levels, or cytokine levels with long COVID was observed.DiscussionThe results suggest that, besides MBL2 polymorphisms promoting a reduction in MBL levels and therefore in its function, they may also contribute to the development of a more intense inflammatory process responsible for the severity of COVID-19
Pervasive gaps in Amazonian ecological research
Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4
While the increasing availability of global databases on ecological communities has advanced our knowledge
of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In
the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of
Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus
crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced
environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian
Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by
2050. This means that unless we take immediate action, we will not be able to establish their current status,
much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio
Pervasive gaps in Amazonian ecological research
Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost