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PRISM: predicting resilience of GPU applications using statistical methods., , , , and . SC, page 69:1-69:14. IEEE / ACM, (2018)Valar: a benchmark suite to study the dynamic behavior of heterogeneous systems., , , and . GPGPU@ASPLOS, page 54-65. ACM, (2013)Hardware Support for Scratchpad Memory Transactions on GPU Architectures., , , , , and . Euro-Par, volume 10417 of Lecture Notes in Computer Science, page 273-286. Springer, (2017)PCFI: Program Counter Guided Fault Injection for Accelerating GPU Reliability Assessment., , , and . DATE, page 308-311. IEEE, (2019)Analyzing power efficiency of optimization techniques and algorithm design methods for applications on heterogeneous platforms., , , , and . Int. J. High Perform. Comput. Appl., 28 (3): 319-334 (2014)MGSim + MGMark: A Framework for Multi-GPU System Research., , , , , , , , , and 3 other author(s). CoRR, (2018)Obtaining FPGA soft error rate in high performance information systems., , , and . Microelectron. Reliab., 49 (5): 551-557 (2009)AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators., , , , , , , , and . CGO, page 143-157. IEEE, (2024)Source level transformations to improve I/O data partitioning., and . SNAPI@PACT, page 27-35. ACM, (2003)Subsequence Matching on Structured Time Series Data., , , , , and . SIGMOD Conference, page 682-693. ACM, (2005)