A high performance, open source SATA2 core

Mendon, Ashwin A.; Huang, Bin; Sass, Ron

doi:10.1109/fpl.2012.6339139

Cited by 12 publications

(6 citation statements)

References 7 publications

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“…In contrast, our platform supports thousands of distributed reconfigurable active SSD nodes. The complexity of the services required in our reconfigurable RASSD node and within our distributed platform middleware layer has made it necessary to adopt an embedded Linux node operating system [Mendon 2012] and a specialized middleware [Jomaa 2013] that hides the complexity of the RASSD hardware. In this work, we propose new middleware that extends Hadoop, configures the RASSD node, and finds and processes the distributed data using an API abstraction layer.…”

Section: Active Storagementioning

confidence: 99%

Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters

Kaitoua

Hajj

Saghir

et al. 2014

ACM Trans. Archit. Code Optim.

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In this article, we propose new extensions to Hadoop to enable clusters of reconfigurable active solid-state drives (RASSDs) to process streaming data from SSDs using FPGAs. We also develop an analytical model to estimate the performance of RASSD clusters running under Hadoop. Using the Hadoop RASSD platform and network simulators, we validate our design and demonstrate its impact on performance for different workloads taken from Stanford's Phoenix MapReduce project. Our results show that for a hardware acceleration factor of 20×, compute-intensive workloads processing 153MB of data can run up to 11× faster than a standard Hadoop cluster.

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Section: Active Storagementioning

confidence: 99%

Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters

Kaitoua

Hajj

Saghir

et al. 2014

ACM Trans. Archit. Code Optim.

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“…They enable the implementation of an inter-chip communication on the basis of protocols like Aurora [2]- [4] or Interlaken [5], [6], and of a tight integration into a host system using QPI [7], [8], PCIe [9]-[11], RapidIO [12], [13] or even Gigabit Ethernet [14]. Also, domain-specific interconnect standards like SATA [15], [16] for the access of storage resources can be implemented.…”

Section: Introductionmentioning

confidence: 99%

Ready PCIe data streaming solutions for FPGAs

Preußer

Spallek

2014

2014 24th International Conference on Field Programmable Logic and Applications (FPL)

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The PCIe attachment of FPGA accelerators within host workstations is convenient and offers a high-performance direct integration. FPGA-boards designed and equipped as PCIe extension cards are available off-the-shelf. This paper gives an overview on options to provide data streaming abstractions in user applications using PCIe technology. RIFFA and Xillybus are straightforward implementations, which aim at providing ready solutions at this level. This paper will describe these platforms and evaluate them in terms of their ease of use, their perceived maturity and the performance metrics bandwidth and latency. It concludes with providing a brief guideline on how to select the proper platform depending on the usage context.

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“…Our Fig. 7 Embedded MicroBlaze access to the SATA2 Core and DRAM [24] architecture combines a varying amount of FSL channels, along with the Ethernet and SATA controllers, to form a flexible and easily controllable data-path for hardware acceleration (Fig. 8).…”

Section: Hw/sw Partitioned Architecturementioning

confidence: 99%

“…However, the Groundhog SATA controller, which although allows for a lot of design flexibility to interface directly with its SATA command layer via custom hardware, is not capable of being connected directly to a PLB interface without significant design additions. As such, the SATA2 controller [24], another open source SATA controller that is more amenable to embedded solutions is adopted in this section. Both controllers are comparable in their SATA 4 KiB write tests, with the SATA2 controller performing slightly worse using comparable SSDs.…”

Section: Dialign Implemented With Software Io Interfacesmentioning

confidence: 99%

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IO and data management for infrastructure as a service FPGA accelerators

Moorthy

Gopalakrishnan

2017

J Cloud Comp

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We describe the design of a non-operating-system based embedded system to automate the management, reordering, and movement of data produced by FPGA accelerators within data centre environments. In upcoming cloud computing environments, where FPGA acceleration may be leveraged via Infrastructure as a Service (IaaS), end users will no longer have full access to the underlying hardware resources. We envision a partially reconfigurable FPGA region that end-users can access for their custom acceleration needs, and a static "template" region offered by the data centre to manage all Input/Output (IO) data requirements to the FPGA. Thus our low-level software controlled system allows for standard DDR access to off-chip memory, as well as DMA movement of data to and from SATA based SSDs, and access to Ethernet stream links. Two use cases of FPGA accelerators are presented as experimental examples to demonstrate the area and performance costs of integrating our data-management system alongside such accelerators. Comparisons are also made to fully custom data management solutions implemented solely in RTL Verilog to determine the tradeoffs in using our system in regards to development time, area, and performance. We find that for a class of accelerators in which the physical data rate of an IO channel is the limiting bottleneck to accelerator throughput, our solution offers drastically reduced logic development time spent on data management without any associated performance losses in doing so. However, for a class of applications where the IO channel is not the bottle-neck, our solution trades off increased area usage to save on design times and to maintain acceptable system throughput in the face of degraded IO throughput.

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A high performance, open source SATA2 core

Cited by 12 publications

References 7 publications

Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters

Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters

Ready PCIe data streaming solutions for FPGAs

IO and data management for infrastructure as a service FPGA accelerators

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