TaskPoint: sampled simulation of task-based programs

Sampled simulation is a mature technique for reducing simulation time of single-threaded programs, but it is not directly applicable to simulation of multi-threaded architectures. Recent multi-threaded sampling techniques assume that the workload assigned to each thread does not change across multiple executions of a program. This assumption does not hold for dynamically scheduled task-based programming models. Task-based programming models allow the programmer to specify program segments as tasks which are instantiated many times and scheduled dynamically to available threads. Due to system noise and variation in scheduling decisions, two consecutive executions on the same machine typically result in different instruction streams processed by each thread. In this paper, we propose TaskPoint, a sampled simulation technique for dynamically scheduled task-based programs. We leverage task instances as sampling units and simulate only a fraction of all task instances in detail. Between detailed simulation intervals we employ a novel fast-forward mechanism for dynamically scheduled programs. We evaluate the proposed technique on a set of 19 task-based parallel benchmarks and two different architectures. Compared to detailed simulation, TaskPoint accelerates architectural simulation with 64 simulated threads by an average factor of 19.1 at an average error of 1.8% and a maximum error of 15.0%.This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493, SEV-2011-00067), the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), the RoMoL ERC Advanced Grant (GA 321253), the European HiPEAC Network of Excellence and the Mont-Blanc project (EU-FP7-610402 and EU-H2020-671697). M. Moreto has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship JCI-2012-15047. M. Casas is supported by the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie Curie Actions of the EUFP7 (contract 2013BP B 00243). T.Grass has been partially supported by the AGAUR of the Generalitat de Catalunya (grant 2013FI B 0058).Peer ReviewedPostprint (author's final draft

Eigenstrain-based reduced order homogenization models for polycrystal plasticity: addressing scalability

In this manuscript, accelerated, sparse and scalable eigenstrain-based reduced order homogenization models have been developed for computationally efficient multiscale analysis of polycrystalline materials. The proposed model is based on the eigenstrain based reduced order homogenization (EHM) approach, which takes the concept of transformation field theory that pre-computes certain microscale information and considers piece-wise constant inelastic response within partitions (e.g., grains) of the microstructure for model order reduction.The acceleration is achieved by introducing sparsity into the linearized reduced order system through selectively considering the interactions between grains based on the idea of grain clustering. The proposed approach results in a hierarchy of reduced models that recovers original EHM, when full range of interactions are considered, and degrades to the Taylor model, when all grain interactions are neglected. The resulting sparse system is solved efficiently using both direct and iterative sparse solvers, both of which show significant efficiency improvements compared to the full EHM. A layer-by-layer neighbor grain clustering scheme is proposed and implemented to define ranges of grain interactions. Performance of the proposed approach is evaluated by comparison with the original full EHM and crystal plasticity finite element (CPFE) simulations