GPAI - General Purpose AI Computing: Publications

Journal articles

• J. Keller, S. Litzinger, and C. Kessler, "Integrating Energy-Optimizing Scheduling of Moldable Streaming Tasks with Design Space Exploration for Multiple Core Types on Configurable Platforms," Journal of Signal Processing Systems, vo. 94, pp. 849-864, 2022. https://doi.org/10.1007/s11265-022-01787-y
• L. Lundberg and H. Grahn, "Research Trends, Enabling Technologies and Application Areas for Big Data," Algorithms, 15(8):280, August 2022. doi: https://doi.org/10.3390/a15080280
• C. Åleskog, H. Grahn, and A. Borg, "Recent Developments in Low-Power AI Accelerators: A Survey," Algorithms, 15(11):419, November 2022. doi: https://doi.org/10.3390/a15110419
• A. Ernstsson, D. Griebler, and C. Kessler, "Assessing Application Efficiency and Performance Portability in Single-Source Programming for Heterogeneous Parallel Systems," International Journal of Parallel Programming, Volume 51, pages 61–82, 2023. doi: https://doi.org/10.1007/s10766-022-00746-1
• S. Litzinger, J. Keller, and C. Kessler, "Packing Multiple Types of Cores for Energy-Optimized Heterogeneous Hardware-Software Co-Design of Moldable Streaming Computations," IEEE Access, vol. 11, pp. 19301-19311, 2023. doi: https://doi.org/10.1109/ACCESS.2023.3248283
• Björn Birath, August Ernstsson, John Tinnerholm, and Christoph Kessler, "High-Level Programming of FPGA-Accelerated Systems with Parallel Patterns," International Journal of Parallel Programming, May 2024. Springer, Open Access. doi: https://doi.org/10.1007/s10766-024-00770-3
• M. Boulasikis, F. Gruian, and RZ. Szász, "Using Machine Learning Hardware to Solve Linear Partial Differential Equations with Finite Difference Methods," International Journal of Parallel Programming, vol. 53, issue 2, article no. 15, 2025. doi: https://doi.org/10.1007/s10766-025-00791-6

Peer-reviewed Conference and Workshop papers

• C. Kessler, J. Keller, and S. Litzinger, "Temperature-Aware Energy-Optimal Scheduling of Moldable Streaming Tasks onto 2D-Mesh-Based Many-Core CPUs with DVFS," Proc. 24th Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP 2021) in conjunction with IPDPS 2021, Portland, Oregon USA, 21 May 2021, Springer LNCS. doi: https://doi.org/10.1007/978-3-030-88224-2_9
• M. Boulasikis, F. Gruian, G. Callanan, and J. Janneck, "Analysing Dataflow Programs with Causation Traces," Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (PACT '22), pp. 534-535, Chicago, Illinois, October 2022. (poster) https://doi.org/10.1145/3559009.3569660
• M. Boulasikis, F. Gruian, G. Callanan and J. Janneck, "Informing Static Mapping and Local Scheduling of Stream Programs with Trace Analysis," 25th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Nancy, France, 2023, pp. 98-103. doi: https://doi.org/10.1109/SYNASC61333.2023.00021
• C. Åleskog, H. Grahn, and A. Borg, "A Comparative Study on Simulation Frameworks for AI Accelerator Evaluation," in 14th International Workshop on Accelerators and Hybrid Emerging Systems (AsHES), pp. 321-328, May 2024. doi: https://doi.org/10.1109/IPDPSW63119.2024.00073
• M. Boulasikis, C. Kessler, F. Gruian, J. W. Keller, and S. Litzinger, "Packet-Type Aware Scheduling of Moldable Streaming Tasks on Multicore Systems with DVFS," Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (SAC '24), pp. 449–451, 2024. doi: https://doi.org/10.1145/3605098.3636081
• M. Boulasikis, F. Gruian and R.-Z. Szász, "Thalassa: Transforming Symbolic PDEs into Tensor-Based Solvers Running on ML Accelerators," 2025 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Milano, Italy, June 2025, pp. 463-472, doi: https://doi.org/10.1109/IPDPSW66978.2025.00072.
• Sehrish Qummar, August Ernstsson, Christoph Kessler, and Oleg Sysoev, "SkePU-DNN: Algorithmic Skeleton Programming for Deep Learning on Heterogeneous Systems," 2025 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Milano, Italy, June 2025, pp. 423-432, doi: https://doi.org/10.1109/IPDPSW66978.2025.00068.