This thesis explores hardware-software co-design for data-intensive applications, targeting the unification of programming models using open standards and exploring experimental techniques for automated query synthesis, and presents X-BQSR, a holistic redesign of genomic base quality score recalibration pipelines.
This work introduces a novel intermediate representation (IR), grounded in relational algebra and sparse iteration theory, that provides a unified abstraction for data and computation that enables workload-agnostic, end-to-end optimizations across diverse analytics applications.
This work introduces Backline, a heterogeneous compilation and runtime framework built within PennyLane and Catalyst that allows researchers to write workloads in high-level languages that map to low-latency execution across diverse distributed target platforms, and demonstrates the compilation and execution of several...
Joseph K. L. Lee, M. Malekmohammadi, Hong-Sheng Zheng et al.· 0 citations
The resulting framework provides a vendor-neutral architectural blueprint that systematically connects scalability requirements, platform responsibilities, operational controls, and AI lifecycle needs, enabling engineering teams to evaluate and evolve data and AI platforms incrementally.
Santoshi Maddali· World Journal of Advanced Re...· 0 citations
The core design goal is to let users decide the application’s hardware mapping and orchestration by editing only the high-level recipe—without modifying the maintained source code or binding the application to a particular runtime system.
Youngjun Lee, Klaus Weide, Wesley Kwiecinski et al.· The international journal of...· 1 citation
It is demonstrated that LLMs provided with specific optimization goals achieve better measured performance and validity rates when generating C code compared to creating computation pipelines and optimization schedules with established frameworks, suggesting that future development should explore alternative approaches...
Jiří Klepl, Matyáš Brabec, Martin Kruliš· 0 citations
Loom, a tuning-free symbolic compiler framework for tile-based SPMD programs on spatial dataflow architectures, is presented, suggesting that hardware-derived symbolic compilation provides a retargetable alternative to profiling-based tuning for spatial dataflow architectures while remaining interpretable by keeping op...
He-Ru Wang, Wei Li, Zhen-Yu Bai et al.· 0 citations
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