Sigmoid: An auto-tuned load balancing algorithm for heterogeneous systems

Pérez, Borja; Stafford, Esteban; Bosque, José Luis; Beivide, Ramón

doi:10.1016/j.jpdc.2021.06.003

Cited by 12 publications

(1 citation statement)

References 39 publications

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“…In the evaluation section, we will conduct a comparison study between a representative offline machine learning-based approach and a sampling-based approach similar to [30] but applicable beyond the ABS domain. Other works [14,18,21] proposed novel adaptive scheduling mechanisms to partition the workload at the data level aiming at achieving load-balancing or power-saving. Many frameworks built based on these designs [8,16,19] can operate directly on OpenCL kernels.…”

Section: Related Workmentioning

confidence: 99%

OptCL: A Middleware to Optimise Performance for High Performance Domain-Specific Languages on Heterogeneous Platforms

Xiao

Andelfinger

Cai

et al. 2022

Algorithms and Architectures for Parallel Processing

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Programming on heterogeneous hardware architectures using OpenCL requires thorough knowledge of the hardware. Many High-Performance Domain-Specific Languages (HPDSLs) are aimed at simplifying the programming efforts by abstracting away hardware details, allowing users to program in a sequential style. However, most HPDSLs still require the users to manually map compute workloads to the best suitable hardware to achieve optimal performance. This again calls for knowledge of the underlying hardware and trial-and-error attempts. Further, very often they only consider an offloading mode where computeintensive tasks are offloaded to accelerators. During this offloading period, CPUs remain idle, leaving parts of the available computational power untapped. In this work, we propose a tool named OptCL for existing HPDSLs to enable a heterogeneous co-execution mode when capable where CPUs and accelerators can process data simultaneously. Through a static analysis of data dependencies among compute-intensive code regions and performance predictions, the tool selects the best execution schemes out of purely CPU/accelerator execution or co-execution. We show that by enabling co-execution on dedicated and integrated CPU-GPU systems up to 13× and 21× speed-ups can be achieved.

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Section: Related Workmentioning

confidence: 99%

OptCL: A Middleware to Optimise Performance for High Performance Domain-Specific Languages on Heterogeneous Platforms

Xiao

Andelfinger

Cai

et al. 2022

Algorithms and Architectures for Parallel Processing

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Equalizer: Energy‐efficient machine learning‐based heterogeneous cluster load balancer

Rahmani,

Belalem,

Mahmoudi

et al. 2024

Concurrency and Computation

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SummaryHeterogeneous systems deliver high computing performance when effectively utilized. It is crucial to execute each application on the most suitable device while maintaining system balance. However, achieving equal distribution of the computing load is challenging due to variations in computing power and device architectures within the system. Moreover, scheduling applications at real‐time further complicates this task, as prior information about the submitted applications is absent. In this context, we introduce “Equalizer,” a real‐time load balancer for heterogeneous systems. “Equalizer” leverages machine learning to continuously monitor the system's state, predicting optimal devices for application execution at runtime. It assigns applications to devices that minimize system imbalance. To quantify system imbalance, we propose a novel metric that reflects the disparity in computing loads across the system's devices. This metric is calculated using predicted execution times of applications. To validate the performance of “Equalizer,” we conducted a comparative study against widely adopted approaches, namely Round Robin and Device Suitability. The experiments were performed on a heterogeneous cluster comprising a master host and three slave servers, equipped with a total of 4 central processing units (CPUs) and 4 graphics processing units (GPUs). All approaches were deployed on the cluster and evaluated using three distinct workloads categorized by their computing intensity: medium intensity, heavy intensity, and a combination of heavy and medium intensity, simulating real‐world scenarios. Each workload consisted of a set of 80 OpenCL applications with varying input data sizes. The experimental results demonstrate that “Equalizer” effectively minimized the system's imbalance, reduced the idle time of devices, and eliminated overloads. Moreover, “Equalizer” exhibited significant improvements in workload execution time, resource utilization, throughput, and energy consumption. Across all tested scenarios, “Equalizer” consistently outperformed alternative approaches, showcasing its robustness, adaptability to dynamic environments, and applicability in real‐world practice.

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