Monolithic GPU architectures face growing limitations due to power density, yield issues, and manufacturing complexity, motivating a shift toward multi-chiplet designs. While promising, these architectures struggle with workloads exhibiting irregular memory access patterns, where static data placement is often insufficient. Though data locality can help, it does not adapt well to dynamic access behaviour, leading to performance degradation. This paper introduces REX, a runtime mechanism that migrates threads to the chiplet where their data resides, adapting dynamically to the generated memory access patterns with a fine granularity. By relocating computation instead of data, REX improves locality and minimises remote memory accesses, which are especially costly in multi-chiplet environments. As a result, it reduces inter-chiplet traffic and scales efficiently with the number of chiplets. On irregular workloads, the solution demonstrates consistent performance gains, averaging a 13 % speedup, with improvements reaching up to 38 %. Moreover, its scalability with chiplet count is particularly noteworthy, delivering a 25 % average gain, and peaking at an impressive 84 % in the most favourable scenarios.

Abstract Nowadays, theArtificial Intelligent (AI)techniques are applied in enterprise software to solveBig DataandBusiness Intelligence (BI)problems. But most AI techniques are computationally excessive, and they become unfeasible for common business use. Therefore, specific high performance computing is needed to reduce the response time and make these software applications viable on an industrial environment. The main objective of this paper is to demonstrate the improvement of an aquaculture BI tool based in AI techniques, using parallel programming. This tool, called AquiAID, was created by the research group of Economic Management for the Sustainable Development of Primary Sector of the Universidad de Cantabria. The parallelisation reduces the computation time up to 60 times, and the energy efficiency by 600 times with respect to the sequential program. With these improvements, the software will improve the fish farming management in aquaculture industry.

Modern High Performance Computing (HPC) clusters often comprise a huge amount of computing resources of different capabilities, making them heterogeneous and difficult to manage. In addition, they must deal with a wide range of applications with different requirements. All this poses a great challenge to the workload managers that assign applications to resources. There are many new proposals to overcome this challenge, including some that employ Deep Reinforcement Learning (DRL) techniques. This paper proposes a novel simulation framework for the study of workload managers, that has been conceived to foster the study of workload managers based on DRL techniques. Its main features include the simulation of heterogeneous clusters based on multicore architectures, taking into account the contention in shared memory access and the energy consumption. A validation of the accuracy and performance of the simulator was made, compared with a real environment based on Slurm. This shows good accuracy of the results, with a relative error below 5% in makespan and 10% in energy consumption, and speedups up to 200.