Workload Placement on Heterogeneous CPU-GPU Systems

Summary: Survey of workload placement strategies for heterogeneous CPU–GPU systems, analyzing tradeoffs among processor characteristics, workload traits, data locality, implementation variants, and runtime prediction for performance-cost optimization. Identifies open challenges and future research directions for multi-device code and placement policies. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: he5c88b8b7188b4c8
Venue: VLDB
Year: 2024
Pagerank: 5.5025409e-05
Overall Rank: 7,523 | 49.43%
DOI: 10.14778/3685800.3685845

Incoming Non-self Citations Over Time

Authors

1. Marcos N. L. Carvalho (Athena Research Center; National and Kapodistrian University of Athens; Polytechnic University of Catalonia)
2. Alkis Simitsis (Athena Research Center)
3. Anna Queralt (Barcelona Supercomputing Center; Polytechnic University of Catalonia)
4. Oscar Romero (Polytechnic University of Catalonia)

BibTeX Citation

@article{carvalho_vldb24,
        title = {{Workload Placement on Heterogeneous CPU-GPU Systems}},
        author = {Carvalho, Marcos N. L. and Simitsis, Alkis and Queralt, Anna and Romero, Oscar},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {12},
        pages = {4241--4244},
        doi = {10.14778/3685800.3685845},
        url = {https://doi.org/10.14778/3685800.3685845},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,023	Hardware Acceleration for Spatial Databases: Building Next-Generation Query Engines Using Ray Tracing Cores	2026	VLDB	4.9793485e-05
11,127	Flux: Unifying Heterogeneous Infrastructure for Alibaba AnalyticDB	2025	SIGMOD	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1,268	A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics	2020	SIGMOD	0.0001126007
1,747	HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines	2019	VLDB	9.7335416e-05
2,284	Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects	2020	SIGMOD	8.6954168e-05
2,600	Robust Query Processing in Co-Processor-accelerated Databases	2016	SIGMOD	8.2363864e-05
3,655	Tile-based Lightweight Integer Compression in GPU	2022	SIGMOD	7.1260841e-05
3,861	GaccO - A GPU-accelerated OLTP DBMS	2022	SIGMOD	6.9656048e-05
3,888	Orchestrating Data Placement and Query Execution in Heterogeneous CPU-GPU DBMS	2022	VLDB	6.945725e-05
3,935	The Case For Heterogeneous HTAP	2017	CIDR	6.9166006e-05
4,137	The Art of Balance: A RateupDB Experience of Building a CPU/GPU Hybrid Database Product	2021	VLDB	6.7861661e-05
4,199	Adaptive Work Placement for Query Processing on Heterogeneous Computing Resources	2017	VLDB	6.7410637e-05
5,132	Ocelot/HyPE: Optimized Data Processing on Heterogeneous Hardware	2014	VLDB	6.2597754e-05
5,172	CGgraph: An Ultra-fast Graph Processing System on Modern Commodity CPU-GPU Co-processor	2024	VLDB	6.2439642e-05
6,489	HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics	2023	CIDR	5.7676507e-05
8,217	Enabling Transparent Acceleration of Big Data Frameworks Using Heterogeneous Hardware	2022	VLDB	5.3763878e-05

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