The significant overhead needed to transfer the data between CPUs and memory devices is one of the hottest issues in many areas of computing, such as database management systems. Disaggregated computing on the memory devices is being highlighted as one promising approach. In this work, we introduce a new near-memory acceleration scheme for in-memory database operations, called Acceleration DIMM (AxDIMM). It behaves like a normal DIMM through the standard DIMM-compatible interface, but has embedded computing units for data-intensive operations. With the minimized data transfer overhead, it reduces CPU resource consumption, relieves the memory bandwidth bottleneck, and boosts energy efficiency. We implement scan operations, one of the most data-intensive database operations, within AxDIMM and compare its performance with SIMD (Single Instruction Multiple Data) implementation on CPU. Our investigation shows that the acceleration achieves 6.8x more throughput than the SIMD implementation.

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Improving In-Memory Database Operations with Acceleration DIMM (AxDIMM)

With the advent of cloud computing, where computational resources are expensive and data movement needs to be secured and minimized, database management systems need to reconsider their architecture to accommodate such requirements. In this paper, we present our analysis, design and evaluation of an FPGA-based hardware accelerator for offloading compression and encryption for SAP HANA, SAP's Software-as-a-Service (SaaS) in-memory database. Firstly, we identify expensive data-transformation operations in the I/O path. Then we present the design details of a system consisting of compression followed by different types of encryption to accommodate different security levels, and identify which combinations maximize performance. We also analyze the performance benefits of offloading decryption to the FPGA followed by decompression on the CPU. The experimental evaluation using SAP HANA traces shows that analytical engines can benefit from FPGA hardware offloading. The results identify a number of important trade-offs (e.g., the system can accommodate low-latency secured transactions to high-performance use cases or offer lower storage cost by also compressing payloads for less critical use cases), and provide valuable information to researchers and practitioners exploring the nascent space of hardware accelerators for database engines.

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Hardware Acceleration of Compression and Encryption in SAP HANA

While hardware disaggregation is considered the "next big thing" providing unique opportunities for database systems, the pipeline-based execution model is state-of-the-art in modern query engines on monolithic systems. Within this paper, we propose a lightweight way of adapting this pipeline-based model to disaggregated memory systems to soften the inherent overhead induced by arbitrary memory accesses. Instead of executing pipelines in strict isolation including a pipeline-local data transfer, we group pipelines with similar data access characteristics of concurrently running queries into pipeline groups . Each such pipeline group is then executed sep-arately, but shared data across pipelines within each group is only transferred once from memory resources to compute resources and potentially re-used multiple times. This method dramatically re-duces redundant data transfers and – in combination with a suitable caching strategy as well as a fast communication layer – increases the performance significantly in comparison to traditional pipeline-based execution of multiple queries.

Pipeline Group Optimization on Disaggregated Systems

CXL interface is the up-to-date technology that enables effective memory expansion by providing a memory-sharing protocol in configuring heterogeneous devices. However, its limited physical bandwidth can be a significant bottleneck for emerging data-intensive applications. In this work, we propose a novel CXL-based memory disaggregation architecture with a real-world prototype demonstration, which overcomes the bandwidth limitation of the CXL interface using near-data processing. The experimental results demonstrate that our design achieves up to 1.9× better performance/power efficiency than the existing CPU system. 

Computational CXL-Memory Solution for Accelerating Memory-Intensive Applications

Today's computing systems require moving data back-and-forth between computing resources (e.g., CPUs, GPUs, accelerators) and off-chip main memory so that computation can take place on the data. Unfortunately, this data movement is a major bottleneck for system performance and energy consumption [1], [2]. One promising execution paradigm that alleviates the data movement bottleneck in modern and emerging applications is processing-in-memory (PIM) [2]–[12], where the cost of data movement to/from main memory is reduced by placing computation capabilities close to memory. In the data-centric PIM paradigm, the logic close to memory has access to data with significantly higher memory bandwidth, lower latency, and lower energy consumption than processors/accelerators in existing processor-centric systems.

Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases

Limited memory volume is always a performance bottleneck in an in-memory database management system (IMDBMS) as the data size keeps increasing. To overcome the physical memory limitation, heterogeneous and disaggregated computing platforms are proposed, such as Gen-Z, CCIX, OpenCAPI, and CXL. In this work, we introduce flexible CXL memory expansion using a CXL type 3 prototype and evaluate its performance in an IMDBMS. Our evaluation shows that CXL memory devices interfaced with PCIe Gen5 are appropriate for memory expansion with nearly no throughput degradation in OLTP workloads and less than 8% throughput degradation in OLAP workloads. Thus, CXL memory is a good candidate for memory expansion with lower TCO in IMDBMSs.

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Enabling CXL Memory Expansion for In-Memory Database Management Systems

Despite the increase of memory capacity and CPU computing power, memory performance remains the bottleneck of in-memory DBMS due to ever-increasing data volumes and application demands. Since the scale of data workload has outpaced traditional CPU caches and memory bandwidth, it is essential to optimize data movement from memory to computing units. In this work, we present a near-memory Database Accelerator (DBA) framework that offloads data-intensive database operations via or to a near-memory computation engine. DBA’s system architecture includes a DBA software module/driver and memory module with DBA engine. We build a Proof-of-Concept (PoC) of DBA using FPGAs with attached DIMMs, and then conduct an experimental evaluation.

Optimizing Data Movement with Near-Memory Acceleration of In-memory DBMS.

The separation and independent scalability of compute and memory is one of the crucial aspects for modern in-memory database systems (IMDBMSs) in the cloud. The new, cache-coherent memory interconnect Compute Express Link (CXL) promises elastic memory capacity through memory pooling. In this work, we adapt the well-known IMDBMS, SAP HANA, for memory pools by features of table data placement and operational heap memory allocation on far memory, and study the impact of the limited bandwidth and higher latency of CXL. Our results show negligible performance degradation for TPC-C. For the analytical workloads of TPC-H, a notable impact on query processing is observed due to the limited bandwidth and long latency of our early CXL implementation. However, our emulation shows it would be acceptably smaller with the improved CXL memory devices.

Donghun Lee

Papers

Enabling CXL Memory Expansion for In-Memory Database Management Systems

Improving In-Memory Database Operations with Acceleration DIMM (AxDIMM)

Optimizing Data Movement with Near-Memory Acceleration of In-memory DBMS.

Elastic Use of Far Memory for In-Memory Database Management Systems