TY - JOUR A1 - Ravedutti Lucio Machado, Rafael A1 - Schmitt, Jonas A1 - Eibl, Sebastian A1 - Eitzinger, Jan A1 - Leißa, Roland A1 - Hack, Sebastian A1 - Pérard-Gayot, Arsène A1 - Membarth, Richard A1 - Köstler, Harald T1 - tinyMD: Mapping molecular dynamics simulations to heterogeneous hardware using partial evaluation JF - Journal of Computational Science UR - https://doi.org/10.1016/j.jocs.2021.101425 KW - Molecular dynamics KW - Partial evaluation KW - High performance computing KW - Load balancing Y1 - 2021 UR - https://doi.org/10.1016/j.jocs.2021.101425 SN - 1877-7503 VL - 2021 IS - 54 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Özkan, M. Akif A1 - Pérard-Gayot, Arsène A1 - Membarth, Richard A1 - Slusallek, Philipp A1 - Leißa, Roland A1 - Hack, Sebastian A1 - Teich, Jürgen A1 - Hannig, Frank T1 - AnyHLS: High-Level Synthesis with Partial Evaluation JF - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (ITCSDI) UR - https://doi.org/10.1109/TCAD.2020.3012172 KW - Field programmable gate arrays KW - functional programming KW - high level synthesis KW - image processing Y1 - 2020 UR - https://doi.org/10.1109/TCAD.2020.3012172 SN - 1937-4151 VL - 39 IS - 11 SP - 3202 EP - 3214 PB - IEEE CY - New York ER - TY - JOUR A1 - Pérard-Gayot, Arsène A1 - Membarth, Richard A1 - Leißa, Roland A1 - Hack, Sebastian A1 - Slusallek, Philipp T1 - Rodent: Generating Renderers without Writing a Generator JF - ACM Transactions on Graphics (TOG) UR - https://doi.org/10.1145/3306346.3322955 KW - Rendering KW - Ray-tracing KW - Generator KW - Partial Evaluation Y1 - 2019 UR - https://doi.org/10.1145/3306346.3322955 SN - 1557-7368 SN - 0730-0301 VL - 38 IS - 4 PB - ACM CY - New York 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 - TY - JOUR A1 - Schuler, Manuela A1 - Membarth, Richard A1 - Slusallek, Philipp T1 - XEngine: Optimal Tensor Rematerialization for Neural Networks in Heterogeneous Environments JF - ACM Transactions on Architecture and Code Optimization N2 - Memory efficiency is crucial in training deep learning networks on resource-restricted devices. During backpropagation, forward tensors are used to calculate gradients. Despite the option of keeping those dependencies in memory until they are reused in backpropagation, some forward tensors can be discarded and recomputed later from saved tensors, so-called checkpoints. This allows, in particular, for resource-constrained heterogeneous environments to make use of all available compute devices. Unfortunately, the definition of these checkpoints is a non-trivial problem and poses a challenge to the programmer—improper or excessive recomputations negate the benefit of checkpointing. In this article, we present XEngine, an approach that schedules network operators to heterogeneous devices in low memory environments by determining checkpoints and recomputations of tensors. Our approach selects suitable resources per timestep and operator and optimizes the end-to-end time for neural networks taking the memory limitation of each device into account. For this, we formulate a mixed-integer quadratic program (MIQP) to schedule operators of deep learning networks on heterogeneous systems. We compare our MIQP solver XEngine against Checkmate [12], a mixed-integer linear programming (MILP) approach that solves recomputation on a single device. Our solver finds solutions that are up to 22.5% faster than the fastest Checkmate schedule in which the network is computed exclusively on a single device. We also find valid schedules for networks making use of both central processing units and graphics processing units if memory limitations do not allow scheduling exclusively to the graphics processing unit. UR - https://doi.org/10.1145/3568956 KW - rematerialization KW - integer linear programming KW - neural networks KW - memory management KW - heterogeneous computing Y1 - 2022 UR - https://doi.org/10.1145/3568956 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-29989 SN - 1544-3973 VL - 20 IS - 1 PB - ACM CY - New York ER - TY - JOUR A1 - Xu, Xiang A1 - Wang, Lu A1 - Pérard-Gayot, Arsène A1 - Membarth, Richard A1 - Li, Cuiyu A1 - Yang, Chenglei A1 - Slusallek, Philipp T1 - Temporal Coherence-Based Distributed Ray Tracing of Massive Scenes JF - IEEE Transactions on Visualization and Computer Graphics UR - https://doi.org/10.1109/TVCG.2022.3219982 KW - Computer Graphics KW - Ray Tracing KW - Distributed Graphics Y1 - 2022 UR - https://doi.org/10.1109/TVCG.2022.3219982 SN - 1941-0506 SN - 1077-2626 VL - 30 IS - 2 SP - 1489 EP - 1501 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Xu, Wenguang A1 - Membarth, Richard T1 - Exploration of Efficient Computation for Trajectory Planning via Fixed-Point Arithmetic JF - PARS-Mitteilungen 2025 Y1 - 2025 UR - https://dl.gi.de/items/6b2c0546-1081-40ab-b97b-052b60b8374f SN - 0177-0454 VL - 37 SP - 51 EP - 61 PB - Gesellschaft für Informatik CY - Bonn ER - TY - JOUR A1 - Pfaller, Tobias A1 - Membarth, Richard T1 - Exploiting Data Redundancy in Im2Col Convolutions JF - PARS-Mitteilungen 2025 Y1 - 2025 UR - https://dl.gi.de/items/156c727e-ec90-4809-9ce6-ee27cc89eeb0 SN - 0177-0454 VL - 37 SP - 63 EP - 70 PB - Gesellschaft für Informatik CY - Bonn ER -