Hardware acceleration of compression and encryption in SAP HANA

Chiosa, Monica; Maschi, Fabio; Müller, Ingo; Alonso, Gustavo; May, Norman

doi:10.14778/3554821.3554822

Cited by 14 publications

(8 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In response to the growing emphasis on network communication encryption, there has been a significant push towards securing most of the network traffic 14 . FPGA-based implementations of AES encryption have demonstrated their effectiveness in this context [10]. By offloading the TLS protocol to the FPGA for AES encryption and decryption, in conjunction with the TCP/IP stack, significant performance gains can be achieved compared to deploying an HTTP server on a CPU.…”

Section: Extending This Workmentioning

confidence: 99%

Strega : An HTTP Server for FPGAs

Maschi,

Alonso

2024

ACM Trans. Reconfigurable Technol. Syst.

Self Cite

View full text Add to dashboard Cite

The computer architecture landscape is being reshaped by the new opportunities, challenges and constraints brought by the cloud. On the one hand, high-level applications profit from specialised hardware to boost their performance and reduce deployment costs. On the other hand, cloud providers maximise the CPU time allocated to client applications by offloading infrastructure tasks to hardware accelerators. While it is well understood how to do this for, e.g., network function virtualisation and protocols such as TCP/IP, support for higher networking layers is still largely missing, limiting the potential of accelerators. In this paper, we present S trega , an open-source 1 light-weight HTTP server that enables crucial functionality such as FPGA-accelerated functions being called through a RESTful protocol (FPGA-as-a-Function). Our experimental analysis shows that a single S trega node sustains a throughput of 1.7 M HTTP requests per second with an end-to-end latency as low as 16 μ s, outperforming nginx running on 32 vCPUs in both metrics, and can even be an alternative to the traditional OpenCL flow over the PCIe bus. Through this work, we pave the way for running microservices directly on FPGAs, bypassing CPU overhead and realising the full potential of FPGA acceleration in distributed cloud applications.

show abstract

Section: Extending This Workmentioning

confidence: 99%

Strega : An HTTP Server for FPGAs

Maschi,

Alonso

2024

ACM Trans. Reconfigurable Technol. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this configuration, the ability of the FPGA to process data at line rate is used to maximise the bandwidth of the storage and of the network without requiring a conventional CPU. In such configurations, it is also possible to incorporate additional functionality such as compression and encryption to make the system even more efficient [4].…”

Section: Scalabilitymentioning

confidence: 99%

The Difficult Balance Between Modern Hardware and Conventional CPUs

Maschi

Alonso

2023

Proceedings of the 19th International Workshop on Data Management on New Hardware

Self Cite

View full text Add to dashboard Cite

Research has demonstrated the potential of accelerators in a wide range of use cases. However, there is a growing imbalance between modern hardware and the CPUs that submit the workload. Recent studies of GPUs on real systems have shown that many servers are often needed per accelerator to generate a high enough load so the computing power is leveraged. This fact is often ignored in research, although it often determines the actual feasibility and overall efficiency of a deployment. In this paper, we conduct a detailed study of the possible configurations and overall cost efficiency of deploying an FPGA-based accelerator on a commercial search engine. First, we show that there are many possible configurations balancing the upstream system and the way the accelerator is configured. Of these configurations, not all of them are suitable in practice, even if they provide some of the highest throughput. Second, we analyse the cost of a deployment capable of sustaining the required workload of the commercial search engine. We examine deployments both on-premises and in the cloud with and without FPGAs and with different board models. The results show that, while FPGAs have the potential to significantly improve overall performance, the performance imbalance between their host CPUs and the FPGAs can make the deployments economically unattractive. These findings are intended to inform the development and deployment of accelerators by showing what is needed on the CPU side to make them effective and also to provide important insights into their end-to-end integration within existing systems. CCS CONCEPTS• Hardware → Hardware accelerators; • Computer systems organization → Cloud computing; Client-server architectures; Heterogeneous (hybrid) systems.

show abstract

“…There exist studies that are specifically centered around data, such as examining the compression efficiency of text data [32], image data [33,34], and sensor data [35]. On the other hand, several studies concentrate on particular research domains, such as hardware acceleration [36,37], architectural approaches [38], and database design [39]. For instance, Kimura et al examine how compression affects choices of appropriate database design, such as indexes [39].…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, it does not improve read throughput due to the increased decompression overhead for larger blocks. This problem can be addressed using emerging hardware devices such as hardware accelerators [37] or new memory devices called NVM (Non-Volatile Memory) [54] or PM (Persistent Memory) [55]. This paper is interested in how to exploit NVM for compression.…”

Section: Suggestionsmentioning

confidence: 99%

Requirements and Trade-Offs of Compression Techniques in Key–Value Stores: A Survey

Jaranilla,

Choi

2023

Electronics

View full text Add to dashboard Cite

The prevalence of big data has caused a notable surge in both the diversity and magnitude of data. Consequently, this has prompted the emergence and advancement of two distinct technologies: unstructured data management and data volume reduction. Key–value stores, such as Google’s LevelDB and Meta’s RocksDB, have emerged as a popular solution for managing unstructured data due to their ability to handle diverse data types with a simple key–value abstraction. Simultaneously, a multitude of data management tools have actively adopted compression techniques, such as Snappy and Zstd, to effectively reduce data volume. The objective of this study is to explore how these two technologies influence each other. For this purpose, we first examine a classification of compression techniques and discuss their strength and weakness, especially those adopted by modern key–value stores. We also investigate the internal structures and operations, such as batch writing and compaction, in order to grasp the characteristics of key–value stores. Then, we quantitatively evaluate the compression ratio and performance using RocksDB under diverse compression techniques, block sizes, value sizes, and workloads. Our evaluation shows that compression not only saves storage space but also decreases compaction overhead. It also reveals that compression techniques have their inherent trade-offs, meaning that some provide a better compression ratio, while others yield better compression performance. Based on our evaluation, a number of potential avenues for further research have been identified. These include the exploration of a compression-aware compaction mechanism, selective compression, and revisiting compression granularity.

show abstract

Hardware acceleration of compression and encryption in SAP HANA

Cited by 14 publications

References 50 publications

Strega : An HTTP Server for FPGAs

Strega : An HTTP Server for FPGAs

The Difficult Balance Between Modern Hardware and Conventional CPUs

Requirements and Trade-Offs of Compression Techniques in Key–Value Stores: A Survey

Contact Info

Product

Resources

About