Titlebook: Benchmarking, Measuring, and Optimizing; 15th BenchCouncil In Sascha Hunold,Biwei Xie,Kai Shu Conference proceedings 2024 The Editor(s) (if

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書目名稱Benchmarking, Measuring, and Optimizing影響因子(影響力)




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Postulate

,Generating High Dimensional Test Data for?Topological Data Analysis,old. While based in the field of topology, TDA is primarily vested in the three computational elements: ., ., and .. The focus of this paper is on developing infrastructure to generate synthetic test data suitable to evaluate computational elements of TDA. The objective of this work is to generate t

FUME

,Does AI for?Science Need Another ImageNet or?Totally Different Benchmarks? A?Case Study of?Machine ethods. Traditional AI benchmarking methods struggle to adapt to the unique challenges posed by AI4S because they assume data in training, testing, and future real-world queries are independent and identically distributed, while AI4S workloads anticipate out-of-distribution problem instances. This p

音樂等

Interregnum

,Cross-Layer Profiling of?IoTBench,tailored for IoT applications. The streamlined yet comprehensive system stack of an IoT system is highly suitable for synergistic software and hardware co-design. This stack comprises various layers, including programming languages, frameworks, runtime environments, instruction set architectures (IS

阻止

,MMDBench: A Benchmark for?Hybrid Query in?Multimodal Database, a gap in benchmarking specifically designed for multimodal data, as existing benchmarks primarily focus on traditional and multimodel databases, lacking a comprehensive framework for evaluating systems handling multimodal data. In this paper, we present a novel benchmark program, named MMDBench, sp

牛馬之尿

malign

,A Linear Combination-Based Method to?Construct Proxy Benchmarks for?Big Data Workloads,are unable to finish running on simulators at an acceptable time cost, as simulators are slower 100X–1000X times than physical platform. Moreover, big data benchmarks usually need the support of complex software stacks, which is hard to be ported on the simulators. Proxy benchmarks have the same mic

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細(xì)胞膜

,Automated HPC Workload Generation Combining Statistical Modeling and?Autoregressive Analysis, the restrictions of privacy and confidentiality, real HPC workloads are rarely open for studying. Generating synthetic workloads that mimic real workloads can facilitate related research, such as cluster planning and scheduling. Thus automated HPC workload generation has long been an active researc