arXiv subjects

Carlos García Sánchez

Publications and source records attributed to Carlos García Sánchez.

4 recordsLinked to original sources

arXiv2025

Evaluation of Domain-Specific Architectures for General-Purpose Applications in Apple Silicon

The rise of AI and its growing computational demands have driven the integration of domain-specific accelerators (such as GPUs, TPUs, and NPUs) across the entire computing infrastructure. Following the precedent set by the GPGPU which popularized GPUs for general-purpose tasks, this research asks whether this phenomenon can be replicated with specialized accelerators like NPUs in new contexts. This paper evaluates the potential of the Apple Neural Engine (ANE) designed for high energy efficiency in Machine Learning workloads, in the context of general-purpose HPC applications. We evaluate the performance and energy consumption of classic HPC algorithms such as GEMM, Jacobi or Multigrid methods on Apple's ANE across the M1 and the latest M4 architectures. Results confirm that, when algorithms are properly adapted, the ANE achieves competitive performance (up to 3.8 TFlops on the M4-Pro, comparable to the GPU's 4.7 TFlops on the same SoC for GEMM operation) while demonstrating significantly superior energy efficiency (e.g., GEMM consumes 5.2 Watts on the ANE versus 24 Watts on GPU counterpart in M4 architectures).

cs.PF↗

arXiv2025

Hardware optimization on Android for inference of AI models

The pervasive integration of Artificial Intelligence models into contemporary mobile computing is notable across numerous use cases, from virtual assistants to advanced image processing. Optimizing the mobile user experience involves minimal latency and high responsiveness from deployed AI models with challenges from execution strategies that fully leverage real time constraints to the exploitation of heterogeneous hardware architecture. In this paper, we research and propose the optimal execution configurations for AI models on an Android system, focusing on two critical tasks: object detection (YOLO family) and image classification (ResNet). These configurations evaluate various model quantization schemes and the utilization of on device accelerators, specifically the GPU and NPU. Our core objective is to empirically determine the combination that achieves the best trade-off between minimal accuracy degradation and maximal inference speed-up.

cs.LG↗

arXiv2024

Assessing Opportunities of SYCL for Biological Sequence Alignment on GPU-based Systems

Bioinformatics and Computational Biology are two fields that have been exploiting GPUs for more than two decades, being CUDA the most used programming language for them. However, as CUDA is an NVIDIA proprietary language, it implies a strong portability restriction to a wide range of heterogeneous architectures, like AMD or Intel GPUs. To face this issue, the Khronos Group has recently proposed the SYCL standard, which is an open, royalty-free, cross-platform abstraction layer, that enables the programming of a heterogeneous system to be written using standard, single-source C++ code. Over the past few years, several implementations of this SYCL standard have emerged, being oneAPI the one from Intel. This paper presents the migration process of the SW\# suite, a biological sequence alignment tool developed in CUDA, to SYCL using Intel's oneAPI ecosystem. The experimental results show that SW\# was completely migrated with a small programmer intervention in terms of hand-coding. In addition, it was possible to port the migrated code between different architectures (considering multiple vendor GPUs and also CPUs), with no noticeable performance degradation on 5 different NVIDIA GPUs. Moreover, performance remained stable when switching to another SYCL implementation. As a consequence, SYCL and its implementations can offer attractive opportunities for the Bioinformatics community, especially considering the vast existence of CUDA-based legacy codes.

cs.PL↗

arXiv2023

Comparing Performance and Portability between CUDA and SYCL for Protein Database Search on NVIDIA, AMD, and Intel GPUs

The heterogeneous computing paradigm has led to the need for portable and efficient programming solutions that can leverage the capabilities of various hardware devices, such as NVIDIA, Intel, and AMD GPUs. This study evaluates the portability and performance of the SYCL and CUDA languages for one fundamental bioinformatics application (Smith-Waterman protein database search) across different GPU architectures, considering single and multi-GPU configurations from different vendors. The experimental work showed that, while both CUDA and SYCL versions achieve similar performance on NVIDIA devices, the latter demonstrated remarkable code portability to other GPU architectures, such as AMD and Intel. Furthermore, the architectural efficiency rates achieved on these devices were superior in 3 of the 4 cases tested. This brief study highlights the potential of SYCL as a viable solution for achieving both performance and portability in the heterogeneous computing ecosystem.

cs.PL↗