Muhammad El-Hindi

Johannes Wehrstein, Roman Heinrich, Mihail Stoian et al.

Workload traces from cloud data warehouse providers reveal that standard benchmarks such as TPC-H and TPC-DS fail to capture key characteristics of real-world workloads, including query repetition and string-heavy queries. In this paper, we introduce Redbench, a novel benchmark featuring a workload generator that reproduces real-world workload characteristics derived from traces released by cloud providers. Redbench integrates multiple workload generation techniques to tailor workloads to specific objectives, transforming existing benchmarks into realistic query streams that preserve intrinsic workload characteristics. By focusing on inherent workload signals rather than execution-specific metrics, Redbench bridges the gap between synthetic and real workloads. Our evaluation shows that (1) Redbench produces more realistic and reproducible workloads for cloud data warehouse benchmarking, and (2) Redbench reveals the impact of system optimizations across four commercial data warehouse platforms. We believe that Redbench provides a crucial foundation for advancing research on optimization techniques for modern cloud data warehouses.

Jigao Luo, Nils Boeschen, Muhammad El-Hindi et al.

The AI hardware boom has led modern data centers to adopt HPC-style architectures centered on distributed, GPU-centric computation. Large GPU clusters interconnected by fast RDMA networks and backed by high-bandwidth NVMe storage enable scalable computation and rapid access to storage-resident data. Tensor computation runtimes (TCRs), such as PyTorch, originally designed for AI workloads, have recently been shown to accelerate analytical workloads. However, prior work has primarily considered settings where the data fits in aggregated GPU memory. In this paper, we systematically study how TCRs can support scalable, distributed query processing for large-scale, storage-resident OLAP workloads. Although TCRs provide abstractions for network and storage I/O, naive use often underutilizes GPU and I/O bandwidth due to insufficient overlap between computation and data movement. As a core contribution, we present PystachIO, a prototype of a PyTorch-based distributed OLAP engine that combines fast network and storage I/O with key optimizations to maximize GPU, network, and storage utilization. Our evaluation shows up to 3x end-to-end speedups over existing distributed GPU-based query processing approaches.