Lawrence Benson

Lawrence Benson

PhD Student at Data Engineering Systems Group

Hasso Plattner Institute

About Me

I am a PhD student in the Data Engineering Systems Group at the Hasso Plattner Institute (HPI) in Potsdam under the supervision of Prof. Dr. Tilmann Rabl.

My research focus is on data management with modern hardware. I am passionate about efficiently leveraging hardware in novel system designs. Currently, I am working on persistent memory and next-gen stream processing systems, with multiple published papers at top venues (VLDB, SIGMOD, CIDR, EDBT). I assist with lectures and supervise various student projects, of which multiple have been published.

Before my PhD, I completed my M.Sc. at HPI with a strong focus on databases and stream processing. I wrote my thesis in collaboration with the DIMA Group @ TU Berlin. During my studies I did two internships at Google in California and New York, working on stream processing.

Interests
  • Data Management on Modern Hardware
  • Persistent Memory
  • Next Generation Stream Processing
Education
  • PhD in Computer Science, ongoing

    Hasso Plattner Institute

  • M.Sc. in IT-Systems Engineering, 2019

    Hasso Plattner Institute

Publications

For more information, view my publication page or Google Scholar.


(2022). PerMA-Bench: Benchmarking Persistent Memory Access. @ VLDB ‘22.

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(2022). Darwin: Scale-In Stream Processing. @ CIDR ‘22.

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(2021). Viper: An Efficient Hybrid PMem-DRAM Key-Value Store. @ VLDB ‘21.

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(2021). Maximizing Persistent Memory Bandwidth Utilization for OLAP Workloads. @ SIGMOD ‘21.

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(2021). Drop It In Like It's Hot: An Analysis of Persistent Memory as a Drop-in Replacement for NVMe SSDs. @ DaMoN ‘21.

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(2020). Disco: Efficient Distributed Window Aggregation. @ EDBT ‘20.

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Teaching‎‏‏‎ ‎& Supervision

Check out our group’s page for upcoming courses and thesis ideas.


Student Paper Supervision:

Thesis Supervision:

  • Title TBD, Leon Papke, Master Thesis, 2022
  • Designing a CPU-aware Hash Table for Streaming Joins, Maximilian Böther, Master Thesis, 2022
  • Efficient Data Ingestion for Stream Processing Systems, Richard Ebeling, Master Thesis, 2022
  • R-Tree Data Placement on Persistent Memory, Nils Thamm, Master Thesis, 2021

Lectures:

  • Hardware-Conscious Data Processing (Master, Summer 2022)
  • Big Data Systems (Master, Winter 2021/22)
  • Database Systems 1 (Bachelor, Summer 2020)
  • Big Data Systems (Master, Winter 2019/20)

Projects/Seminars:

  • Data Management on Modern Storage Technologies (Master, Winter 2020/21)
  • Open Source Data Processing (Master, Winter 2020/21)
  • Data Processing on Modern Hardware (Master, Summer 2020)

Master Project Supervision:

  • Stream Processing on Modern Hardware (Winter 2021/22)
  • Processor-Specific Stream Processing Query Compilation (Summer 2021)
  • Compilation Techniques for Dynamic Stream Processing (Summer 2020)
  • Dynamic Stream Processing (Winter 2019/20)