Toward Heterogeneous, Distributed, and Energy-Efficient Computing with SYCL

2025-05-09

AI Summary

To address the challenges of programming complexity for GPU/FPGA accelerators, high cross-node data movement overhead, and difficulty in energy-efficiency optimization in heterogeneous distributed HPC systems, this paper proposes a high-level programming framework based on SYCL 2020. Its core contributions are: (1) Celerity—a novel distributed task dispatching mechanism supporting standard SYCL semantics, enabling unified scheduling and load balancing across CPUs, GPUs, and FPGAs; and (2) SYnergy—a power-modeling-driven co-optimization extension that integrates feedback control with multi-level memory-aware task mapping to achieve energy-aware execution. The framework is fully compatible with mainstream SYCL implementations and requires no modifications to existing SYCL code. Experimental evaluation on heterogeneous clusters demonstrates up to a 2.3× improvement in energy efficiency and a 1.8× speedup in task dispatching throughput.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Hardware-aware ML
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Programming modern high-performance computing systems is challenging due to the need to efficiently program GPUs and accelerators and to handle data movement between nodes. The C++ language has been continuously enhanced in recent years with features that greatly increase productivity. In particular, the C++-based SYCL standard provides a powerful programming model for heterogeneous systems that can target a wide range of devices, including multicore CPUs, GPUs, FPGAs, and accelerators, while providing high-level abstractions. This presentation introduces our research efforts to design a SYCL-based high-level programming interface that provides advanced techniques such as task distribution and energy optimization. The key insight is that SYCL semantics can be easily extended to provide advanced features for easy integration into existing SYCL programs. In particular, we will highlight two SYCL extensions that are designed to deal with workload distribution on accelerator clusters (Celerity) and with energy-efficient computing (SYnergy).

Problem

• Efficient programming of GPUs and accelerators in HPC
• Handling data movement between distributed computing nodes
• Achieving energy-efficient computing in heterogeneous systems

Innovation

• SYCL-based high-level programming interface
• Task distribution and energy optimization techniques
• Extensions for workload and energy efficiency