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: 13814
Venue: VLDB
Year: 2024
Pagerank: 5.2043672e-05
Overall Rank: 9,873 | 32.27%
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 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,691	Flux: Unifying Heterogeneous Infrastructure for Alibaba AnalyticDB	2025	SIGMOD	5.093636e-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
1,466	A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics	2020	SIGMOD	0.0001068941
1,977	HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines	2019	VLDB	9.3641101e-05
2,566	Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects	2020	SIGMOD	8.4116562e-05
2,713	Robust Query Processing in Co-Processor-accelerated Databases	2016	SIGMOD	8.2122726e-05
3,890	GaccO - A GPU-accelerated OLTP DBMS	2022	SIGMOD	7.0435206e-05
3,895	The Case For Heterogeneous HTAP	2017	CIDR	7.0416054e-05
4,144	Tile-based Lightweight Integer Compression in GPU	2022	SIGMOD	6.8744592e-05
4,243	Orchestrating Data Placement and Query Execution in Heterogeneous CPU-GPU DBMS	2022	VLDB	6.8073248e-05
4,376	Adaptive Work Placement for Query Processing on Heterogeneous Computing Resources	2017	VLDB	6.7364442e-05
4,603	The Art of Balance: A RateupDB Experience of Building a CPU/GPU Hybrid Database Product	2021	VLDB	6.6105578e-05
5,075	CGgraph: An Ultra-fast Graph Processing System on Modern Commodity CPU-GPU Co-processor	2024	VLDB	6.3740128e-05
5,092	Ocelot/HyPE: Optimized Data Processing on Heterogeneous Hardware	2014	VLDB	6.3669454e-05
6,487	HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics	2023	CIDR	5.8651793e-05
8,104	Enabling Transparent Acceleration of Big Data Frameworks Using Heterogeneous Hardware	2022	VLDB	5.4864891e-05

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