Jose Luis Bosque

The performance and energy efficiency offered by heterogeneous systems are highly useful for modern C++ applications, but the technological variety demands adequate portability and programmability. Initiatives such as Intel oneAPI facilitate the exploitation of Intel CPUs and GPUs, but not NVIDIA GPUs, which are present in systems of all kinds and are necessarily leveraged by CUDA technology. Frequently, only GPUs are used, leaving the CPU for management tasks, with the consequent loss of energy and system utilization. In this work, the CoexecutorRuntime system design and API are extended to transparently integrate backends of diverse technologies, unifying offloading mechanisms under a consistent co-execution API and scheduling runtime. Moreover, CPU-GPU co-execution of hybrid technologies is enabled to ensure performance portability. Experimental results show performance improvements for all programs studied, achieving average efficiencies of 0.91 and speedups of 1.31 over using only the GPU.

In recent years, energy consumption has become a limiting factor in the evolution of high-performance computing (HPC) clusters in terms of environmental concern and maintenance cost. The computing power of these clusters is increasing, together with the demands of the workloads they execute. A key component in HPC systems is the workload manager, whose operation has a substantial impact on the performance and energy consumption of the clusters. Recent research has employed machine learning techniques to optimise the operation of this component. However, these attempts have focused on homogeneous clusters where all the cores are pooled together and considered equal, disregarding the fact that they are contained in nodes and that they can have different performances. This work presents an intelligent job scheduler based on deep reinforcement learning that focuses on reducing energy consumption of heterogeneous HPC clusters. To this aim it leverages information provided by the users as well as the power consumption specifications of the compute resources of the cluster. The scheduler is evaluated against a set of heuristic algorithms showing that it has potential to give similar results, even in the face of the extra complexity of the heterogeneous cluster.

The Bicameral Cache is a cache organization proposal for a vector architecture that segregates data according to their access type, distinguishing scalar from vector references. Its aim is to avoid both types of references from interfering in each other's data locality, with a special focus on prioritizing the performance on vector references. The proposed system incorporates an additional, non-polluting prefetching mechanism to help populate the long vector cache lines in advance to increase the hit rate by further exploiting the spatial locality on vector data. Its evaluation was conducted on the Cavatools simulator, comparing the performance to a standard conventional cache, over different typical vector benchmarks for several vector lengths. The results proved the proposed cache speeds up performance on stride-1 vector benchmarks, while hardly impacting non-stride-1's. In addition, the prefetching feature consistently provided an additional value.

Heterogeneous systems are the go-to solution in computing, ranging from HPC to mobile, due to their excellent performance and energy efficiency. However, using this kind of systems adequately poses challenges. Namely, each of the devices that comprise the system are often considered as independent entities that need to be managed and dispatched work to manually. This represents a significant burden on programming and often results in a fastest-device-only approach, in which compute intensive regions are offloaded to the fastest device available, while the rest of the system idles. This idling represents a waste of computing capabilities that could be leveraged if the workload was co-executed. Software solutions have been proposed to provide transparent co-execution, but they always trade abstraction and ease of use for performance. In general, a higher level of abstraction, which improves programmability, will generate overheads. This paper presents HCoD (Hardware co-execution Dispatcher), a design for a hardware dispatcher to enable transparent co-execution without the overheads in integrated heterogeneous SoCs. The dispatcher distributes the work associated to a single kernel among CPU cores and GPU compute units at runtime, while monitoring co-execution to balance the load and prevent a slow device from delaying computation. HCoD achieves an excellent balance among all the compute elements and improves performance by an average of 14%, by transparently leveraging the computing capabilities already available in the hardware.

This article delves into the critical realm of modern computer cluster management. It focuses on the effect that the increasing heterogeneity of the clusters has on the workload managers. The proposed schedulers consider node properties instead of job properties to make decisions, which is something not currently done by mainstream scheduling algorithms. In order to increase the knowledge in this topic, this paper proposes two novel algorithms whose main task is to choose the best compute nodes to schedule the incoming jobs. To this effect, they exclusively take into account the properties of the nodes, instead of the common trend of considering the properties of the jobs. The experimental results show that these algorithms outperform well-known heuristic algorithms found in the literature.

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.