Memory tiering is an effective strategy for enhancing the memoryto-core ratio in modern compute servers.Intel's Flat Memory Mode (FLAT) represents a cutting-edge, hardware-managed memory tiering system for CXL memory, operating at cache-line (64B) granularity with yet unknown potential for database management systems.In this paper, we evaluate the performance of FLAT using OLAP workloads in SAP HANA, an in-memory database management system.Our findings demonstrate that flat memory outperforms directly attached CXL memory by leveraging data placement and exploiting locality.However, FLAT currently accepts only a 1:1 ratio between local and CXL memory.When the local memory capacity increases, FLAT operates in a mixed mode (MIXED) containing flat memory and additional dedicated memory with remaining local memory in a separate NUMA node.Our evaluation of MIXED shows that performance degradation depends on access patterns and the amount of exploited data locality when moving table data to flat memory or allocating heap memory in flat memory.To address the configuration complexity of two separate NUMA nodes in MIXED, we propose a new CONSOLIDATED mode that merges two NUMA nodes.Our evaluation shows that the consolidated mode results in negligible performance degradation, regardless of the ratio between dedicated memory and flat memory, and requires no software modifications at the application level.

CXL-based disaggregated memory systems offer options to expand the memory beyond the limits of a single server via cache-coherent memory expansion cards or memory pools. Especially, In-Memory Database Management Systems (IMDBMSs) can benefit from alleviating two critical constraints: (1) limited memory capacity in a server and (2) long restart time during failover to reload data to memory. However, the usage and effectiveness of CXL memory in enterprise-scale IMDBMSs has yet to be validated. In this work---for the first time---we investigate dynamic memory expansion employing commercial CXL memory devices for IMDBMSs. Our detailed performance analysis reveals that the performance impact of higher latency and lower memory bandwidth impact depends on the memory access patterns of data structures (cf. (1)). Additionally, we present the feasibility of CXL shared memory between servers to improve restart times during failover (cf. (2)). Our evaluation shows the effectiveness of CXL memory integrated into the SAP HANA Cloud IMDBMS. OLTP workloads have a negligible performance degradation while OLAP workloads have a wide range of performance degradation. CXL shared memory shows a 40% reduction of the restart time for TPC-H SF10 and 84% potential reduction for TPC-H SF100.

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.

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.

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.