14 citations · 19 across the 6 of their papers we have counts for
6 papers
Over the Memory Wall, Into the Instruction Wall: The New Bottleneck in GPU Data Processing
Sven Hepkema, Bowen Wu, Christos Kozyrakis +2
Datacenter GPUs have seen an order-of-magnitude increase in memory bandwidth with the adoption of newer generations of HBM. Meanwhile, GPU database systems are gaining traction, ma…
Eiger: An Efficient Library for GPU-based Data Analytics
Bowen Wu, Marko Kabić, Sven Hepkema +3
GPUs have become an increasingly attractive platform for accelerating analytical workloads due to their massive parallelism and high memory bandwidth. Recent studies show that in s…
CodegenBench: Can LLMs Write Efficient Code Across Architectures?
Jie Li, Wenzhao Wu, Junqi Hu +5
While large language models (LLMs) have been extensively evaluated on code generation tasks for general-purpose programming and GPU-accelerated environments (e.g., PyTorch, CUDA),…
To GPU or Not to GPU: Vector Search in Relational Engines
Vasilis Mageirakos, Joel André, Marko Kabić +3
Vector search (VS) is now available in most database engines. However, while vector search is a common feature in AI/ML/LLMs where the dominant computing platforms are GPUs, existi…
Cracking Vector Search Indexes
Vasilis Mageirakos, Bowen Wu, Gustavo Alonso
Retrieval Augmented Generation (RAG) uses vector databases to expand the expertise of an LLM model without having to retrain it. The idea can be applied over data lakes, leading to…
Efficiently Processing Joins and Grouped Aggregations on GPUs
Bowen Wu, Dimitrios Koutsoukos, Gustavo Alonso
There is a growing interest in leveraging GPUs for tasks beyond ML, especially in database systems. Despite the existing extensive work on GPU-based database operators, several que…