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1. Understanding the effect of data center resource disaggregation on production DBMSs

   https://doi.org/10.14778/3397230.3397249  

   Zhang, Qizhen ; Cai, Yifan ; Chen, Xinyi ; Angel, Sebastian ; Chen, Ang ; Liu, Vincent ; Loo, Boon Thau ( May 2020 , Proceedings of the VLDB Endowment)

   Resource disaggregation is a new architecture for data centers in which resources like memory and storage are decoupled from the CPU, managed independently, and connected through a high-speed network. Recent work has shown that although disaggregated data centers (DDCs) provide operational benefits, applications running on DDCs experience degraded performance due to extra network latency between the CPU and their working sets in main memory. DBMSs are an interesting case study for DDCs for two main reasons: (1) DBMSs normally process data-intensive workloads and require data movement between different resource components; and (2) disaggregation drastically changes the assumption that DBMSs canmore »rely on their own internal resource management. We take the first step to thoroughly evaluate the query execution performance of production DBMSs in disaggregated data centers. We evaluate two popular open-source DBMSs (MonetDB and PostgreSQL) and test their performance with the TPC-H benchmark in a recently released operating system for resource disaggregation. We evaluate these DBMSs with various configurations and compare their performance with that of single-machine Linux with the same hardware resources. Our results confirm that significant performance degradation does occur, but, perhaps surprisingly, we also find settings in which the degradation is minor or where DDCs actually improve performance.« less
2. Calorimetric classification of track-like signatures in liquid argon TPCs using MicroBooNE data

   https://doi.org/10.1007/JHEP12(2021)153  

   Abratenko, P. ; An, R. ; Anthony, J. ; Asaadi, J. ; Ashkenazi, A. ; Balasubramanian, S. ; Baller, B. ; Barnes, C. ; Barr, G. ; Basque, V. ; et al ( December 2021 , Journal of High Energy Physics)

   A bstract The MicroBooNE liquid argon time projection chamber located at Fermilab is a neutrino experiment dedicated to the study of short-baseline oscillations, the measurements of neutrino cross sections in liquid argon, and to the research and development of this novel detector technology. Accurate and precise measurements of calorimetry are essential to the event reconstruction and are achieved by leveraging the TPC to measure deposited energy per unit length along the particle trajectory, with mm resolution. We describe the non-uniform calorimetric reconstruction performance in the detector, showing dependence on the angle of the particle trajectory. Such non-uniform reconstruction directly affectsmore »the performance of the particle identification algorithms which infer particle type from calorimetric measurements. This work presents a new particle identification method which accounts for and effectively addresses such non-uniformity. The newly developed method shows improved performance compared to previous algorithms, illustrated by a 93.7% proton selection efficiency and a 10% muon mis-identification rate, with a fairly loose selection of tracks performed on beam data. The performance is further demonstrated by identifying exclusive final states in ν μ CC interactions. While developed using MicroBooNE data and simulation, this method is easily applicable to future LArTPC experiments, such as SBND, ICARUS, and DUNE.« less
3. Jigsaw: A Data Storage and Query Processing Engine for Irregular Table Partitioning

   https://doi.org/10.1145/3448016.3457547  

   Kang, Donghe ; Jiang, Ruochen ; Blanas, Spyros ( June 2021 , Proceedings of the 2021 International Conference on Management of Data)

   The physical data layout significantly impacts performance when database systems access cold data. In addition to the traditional row store and column store designs, recent research proposes to partition tables hierarchically, starting from either horizontal or vertical partitions and then determining the best partitioning strategy on the other dimension independently for each partition. All these partitioning strategies naturally produce rectangular partitions. Coarse-grained rectangular partitioning reads unnecessary data when a table cannot be partitioned along one dimension for all queries. Fine-grained rectangular partitioning produces many small partitions which negatively impacts I/O performance and possibly introduces a high tuple reconstruction overhead. Thismore »paper introduces Jigsaw, a system that employs a novel partitioning strategy that creates partitions with arbitrary shapes, which we refer to as irregular partitions. The traditional tuple-at-a-time or operator-at-a-time query processing models cannot fully leverage the advantages of irregular partitioning, because they may repeatedly read a partition due to its irregular shape. Jigsaw introduces a partition-at-a-time evaluation strategy to avoid repeated accesses to an irregular partition. We implement and evaluate Jigsaw on the HAP and TPC-H benchmarks and find that irregular partitioning is up to 4.2× faster than a columnar layout for moderately selective queries. Compared with the columnar layout, irregular partitioning only transfers 21% of the data to complete the same query.« less
4. Hardware Supported Permission Checks on Persistent Objects for Performance and Programmability

   https://doi.org/10.1109/ISCA.2018.00046  

   Wang, Tiancong ; Sambasivam, Sakthikumaran ; Tuck, James ( June 2018 , International Symposium on Computer Architecture)

   Non-Volatile Memory technologies are advancing rapidly and may augment or replace DRAM in future systems. However, a key question is how programmers will use them to construct and manipulate persistent data. One possible approach gives programmers direct access to persistent memory using relocatable persistent pools that hold persistent objects which can be accessed using persistent pointers, called ObjectIDs. Prior work has shown that hardware-supported address translation for ObjectIDs provides significant performance improvement and simplifies programming, however these works did not consider the large overheads incurred to check permissions before accessing persistent objects. In this paper, we identify permission checking inmore »hardware as a critical mechanism that must be included when translating ObjectIDs to addresses in order to simplify programming and fully benefit from hardware translation. To support it, we add a System Persistent Object Table (SPOT) to support translation and permissions checks on ObjectIDs. The SPOT holds all known pools, their physical address, and their permissions information in memory. When a program attempts to access a persistent object, the SPOT is consulted and permissions are verified without trapping to the operating system. We have implemented our new design in a cycle accurate simulator and compared it with software only approaches and prior work. We find that our design offers a compelling 2.9x speedup on average for microbenchmarks that access pools with the RANDOM pattern and 1.4x and 1.8x speedup on TPC-C and vacation, respectively, for the SEPARATE pattern.« less
5. ColumnBurst: a near-storage accelerator for memory-efficient database join queries

   https://doi.org/10.1145/3409963.3410494  

   Sun, Gongjin ; Jun, Sang-Woo ( August 2020 , roceedings of the 11th ACM SIGOPS Asia-Pacific Workshop on Systems)

   We present ColumnBurst, a memory-efficient, near-storage hardware accelerator for database join queries. While the paradigm of near-storage computation has demonstrated performance and efficiency benefits on many workloads by reducing data movement overhead, memory-bound operations such as relational joins on unsorted data have been relatively inefficient with fast modern storage devices, due to the limited capacity and performance of memory available on the near-storage processing engine. ColumnBurst delivers very high performance even on such complex queries, while staying within the memory performance and capacity budget of what is typically already available on off-the-shelf storage devices. ColumnBurst achieves this via a compact,more »hardware implementation of sorting-based group-by aggregation and join algorithms, instead of the conventional hash-based algorithms. We evaluate ColumnBurst using an FPGA-based prototype with 1 GB of slow on-device DDR3 DRAM, and show that on benchmarks including TPC-H queries with join queries on unsorted columns, it outperforms MonetDB on a 6-core i7 with 32 GB of DRAM by over 7x, and ColumnBurst using a near-storage hash join algorithm by 2x.« less