TY - CHAP A1 - Müller, André A1 - Schmidt, Bertil A1 - Membarth, Richard A1 - Leißa, Roland A1 - Hack, Sebastian T1 - AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs T2 - Proceedings of the 36th ACM International Conference on Supercomputing (ICS-2022) UR - https://doi.org/10.1145/3524059.3532376 Y1 - 2022 UR - https://doi.org/10.1145/3524059.3532376 SN - 978-1-4503-9281-5 PB - ACM CY - New York ER - TY - CHAP A1 - Müller, André A1 - Schmidt, Bertil A1 - Hildebrandt, Andreas A1 - Membarth, Richard A1 - Leißa, Roland A1 - Kruse, Matthis A1 - Hack, Sebastian T1 - AnySeq: A High Performance Sequence Alignment Library based on Partial Evaluation T2 - Proceedings: 2020 IEEE 34th International Parallel and Distributed Processing Symposium (IPDPS 2020) UR - https://doi.org/10.1109/IPDPS47924.2020.00109 KW - Libraries KW - Computer architecture KW - Generators KW - Bioinformatics KW - Hardware KW - Field programmable gate arrays KW - Two dimensional displays Y1 - 2020 UR - https://doi.org/10.1109/IPDPS47924.2020.00109 SN - 978-1-7281-6876-0 SN - 1530-2075 SP - 1030 EP - 1040 PB - IEEE CY - Los Alamitos ER - TY - JOUR A1 - Leißa, Roland A1 - Boesche, Klaas A1 - Hack, Sebastian A1 - Pérard-Gayot, Arsène A1 - Membarth, Richard A1 - Slusallek, Philipp A1 - Müller, André A1 - Schmidt, Bertil T1 - AnyDSL: a partial evaluation framework for programming high-performance libraries JF - Proceedings of the ACM on Programming Languages N2 - This paper advocates programming high-performance code using partial evaluation. We present a clean-slate programming system with a simple, annotation-based, online partial evaluator that operates on a CPS-style intermediate representation. Our system exposes code generation for accelerators (vectorization/parallelization for CPUs and GPUs) via compiler-known higher-order functions that can be subjected to partial evaluation. This way, generic implementations can be instantiated with target-specific code at compile time. In our experimental evaluation we present three extensive case studies from image processing, ray tracing, and genome sequence alignment. We demonstrate that using partial evaluation, we obtain high-performance implementations for CPUs and GPUs from one language and one code base in a generic way. The performance of our codes is mostly within 10%, often closer to the performance of multi man-year, industry-grade, manually-optimized expert codes that are considered to be among the top contenders in their fields. UR - https://doi.org/10.1145/3276489 KW - partial evaluation KW - high-performance KW - parallelization KW - vectorization KW - GPU computing KW - library design Y1 - 2018 UR - https://doi.org/10.1145/3276489 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-28104 SN - 2475-1421 VL - 2 IS - OOPSLA PB - ACM CY - New York ER -