Enhancing FPGA-based RDMA-enabled SmartNICs for scale-out computing

To address the burgeoning data growth and expanding workloads/applications, modern data centers are equipped with thousands of network-connected hosts, each featuring CPUs and accelerators like ASICs, FPGAs and GPUs. The high-speed networking requirements have driven the emergence of SmartNIC techno...

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Main Author: Kolekar Aditya Dilip
Other Authors: Kim Tae Hyoung
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/173997
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1739972024-03-15T15:43:19Z Enhancing FPGA-based RDMA-enabled SmartNICs for scale-out computing Kolekar Aditya Dilip Kim Tae Hyoung School of Electrical and Electronic Engineering AMD, Singapore Technical University of Munich Haris Javaid THKIM@ntu.edu.sg Computer and Information Science Engineering SmartNIC Remote direct memory access To address the burgeoning data growth and expanding workloads/applications, modern data centers are equipped with thousands of network-connected hosts, each featuring CPUs and accelerators like ASICs, FPGAs and GPUs. The high-speed networking requirements have driven the emergence of SmartNIC technologies and Remote Direct Memory Access (RDMA). This thesis focuses on FPGA-based RDMA-enabled SmartNICs, more specifically, enhancing features in RecoNIC, an open-sourced adaptive SmartNIC platform from AMD. RecoNIC provides a platform to implement FPGA accelerators while allowing those accelerators to initiate RDMA read/write requests for communication through the host. Relying on CPUs for RDMA control operations typically results in higher read/write latency, especially when transmitting small messages. This work extends RecoNIC to share its RDMA offloading engine with host CPU and FPGA accelerators, as well as adding FPGA-side DRAM support. Moreover, instead of relying on host CPUs to control the RDMA engine, control operations are offloaded onto FPGA accelerators via HLS to significantly reduce RDMA latency, which is crucial for high performance computing. The experiments show significant improvements in the performance of RDMA read/write operations, particularly notable for small payload sizes. For RDMA read operations, near line-rate throughput is achieved at a 4KB payload size with control offload, a considerable improvement compared to the 16KB payload size required without control offload. The latency of read and write operations is reduced by almost 22% and 29%, respectively, demonstrating the tangible benefits of offloading RDMA control operations. Last but not least, most of the work conducted in this thesis has been contributed to the RecoNIC open-source project [25]. Master's degree 2024-03-11T08:51:08Z 2024-03-11T08:51:08Z 2023 Thesis-Master by Coursework Kolekar Aditya Dilip (2023). Enhancing FPGA-based RDMA-enabled SmartNICs for scale-out computing. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/173997 https://hdl.handle.net/10356/173997 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Engineering
SmartNIC
Remote direct memory access
spellingShingle Computer and Information Science
Engineering
SmartNIC
Remote direct memory access
Kolekar Aditya Dilip
Enhancing FPGA-based RDMA-enabled SmartNICs for scale-out computing
description To address the burgeoning data growth and expanding workloads/applications, modern data centers are equipped with thousands of network-connected hosts, each featuring CPUs and accelerators like ASICs, FPGAs and GPUs. The high-speed networking requirements have driven the emergence of SmartNIC technologies and Remote Direct Memory Access (RDMA). This thesis focuses on FPGA-based RDMA-enabled SmartNICs, more specifically, enhancing features in RecoNIC, an open-sourced adaptive SmartNIC platform from AMD. RecoNIC provides a platform to implement FPGA accelerators while allowing those accelerators to initiate RDMA read/write requests for communication through the host. Relying on CPUs for RDMA control operations typically results in higher read/write latency, especially when transmitting small messages. This work extends RecoNIC to share its RDMA offloading engine with host CPU and FPGA accelerators, as well as adding FPGA-side DRAM support. Moreover, instead of relying on host CPUs to control the RDMA engine, control operations are offloaded onto FPGA accelerators via HLS to significantly reduce RDMA latency, which is crucial for high performance computing. The experiments show significant improvements in the performance of RDMA read/write operations, particularly notable for small payload sizes. For RDMA read operations, near line-rate throughput is achieved at a 4KB payload size with control offload, a considerable improvement compared to the 16KB payload size required without control offload. The latency of read and write operations is reduced by almost 22% and 29%, respectively, demonstrating the tangible benefits of offloading RDMA control operations. Last but not least, most of the work conducted in this thesis has been contributed to the RecoNIC open-source project [25].
author2 Kim Tae Hyoung
author_facet Kim Tae Hyoung
Kolekar Aditya Dilip
format Thesis-Master by Coursework
author Kolekar Aditya Dilip
author_sort Kolekar Aditya Dilip
title Enhancing FPGA-based RDMA-enabled SmartNICs for scale-out computing
title_short Enhancing FPGA-based RDMA-enabled SmartNICs for scale-out computing
title_full Enhancing FPGA-based RDMA-enabled SmartNICs for scale-out computing
title_fullStr Enhancing FPGA-based RDMA-enabled SmartNICs for scale-out computing
title_full_unstemmed Enhancing FPGA-based RDMA-enabled SmartNICs for scale-out computing
title_sort enhancing fpga-based rdma-enabled smartnics for scale-out computing
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/173997
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