Memory tiering is an effective strategy for enhancing the memory-to-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.
This paper introduces G-ALP, a GPU-optimized version of ALP, which is a recent and state-of-the-art compression scheme for floating-point. This GPU-optimization is based on two core ideas. First, all parts of the decoding process must be fully data-parallelized. In this paper, we fully data-parallelize exception patching, which typically applies to only 1% of the data. While patching has negligible performance cost on CPUs, it can become the main bottleneck on GPUs if it is not data-parallel. Second, the decoding API must minimize its register footprint, a highly scarce resource on GPUs, and hence deliver just one value-at-a-time. Our unique aim is to integrate G-ALP decoding into GPU kernels that consume data from global memory, rather than let decompression be a separate kernel. We consider these two ideas general guidelines for future GPU-optimized lightweight encodings, and a significant evolution of our new FastLanes file format, making it GPU-friendly. We extensively test G-ALP in a series of microbenchmarks and evaluate its performance on an NVIDIA V100 GPU and an NVIDIA RTX4070 Super Ti GPU, demonstrating superior performance compared to NVIDIA nvCOMP and ndzip in both decoding and filtering queries.
GPU-accelerated database systems promise significant performance gains for analytical workloads, but their adoption remains limited due to fundamental data movement bottlenecks when the dataset does not fit in GPU memory. Compression can significantly accelerate data movement, but when the dataset does not fit in GPU memory, the choice of compression algorithm (lightweight or heavyweight) can have a wide range of effects, including a potential slowdown compared to uncompressed data. This paper analyzes the impact of compression on query execution performance for larger-than-GPU-memory datasets stored on arrays of NVMe SSDs. We integrate pipelined data movement and on-GPU decompression into a GPU query processing engine. Our experiments show that when storage bandwidth is limited, compression always accelerates query execution, with lightweight compression incurring a higher speedup than heavyweight compression. As storage bandwidth is increased, however, compression can incur a speedup or a slowdown, depending on the processing intensity of the query. We quantify the impact of compression on query execution time with a microbenchmark that varies both storage bandwidth as well as the selectivity and number of joins. We find that heavyweight compression suffers from performance degradation with less processing-intensive queries and lower storage bandwidths than lightweight compression. We further analyze the compression algorithm’s impact on GPU utilization using an extended roofline model incorporating the SSD-to-GPU bandwidth bottleneck. For queries from the Star Schema Benchmark, the results show that while lightweight compression drastically increases the count of executed instructions, the improvement in effective storage bandwidth results in improved query execution times.
