Performance of graph algorithms often depends both on the underlying hardware architecture and on structural properties of the input graph. Optimizations that deliver high performance on one class of graphs, such as hypersparse graphs with low average degree, can degrade performance on other classes, for example denser graphs with high average degree. In this work, we investigate sparsity-aware GPU kernel selection for computing the Jaccard similarity index, a measure of neighborhood overlap in graph datasets. In our kernel selection approach, we use the vertex-centric Jaccard similarity implementation from the cuGraph library as the baseline and include both vertex- and edge-centric variants of this kernel, with set-intersection algorithms varying between two-pointer linear search, binary search, and adaptive dynamic search. We use 80 real-world graphs in our evaluation with variation in average degree, maximum degree, Gini index, and average intersection cost. A random forest classifier, trained on a subset of these graphs on an NVIDIA A100 GPU, achieves 88.8% inference accuracy in predicting the fastest kernel. Kernels selected by the classifier achieve a 4.37x mean speedup over the vertex-centric cuGraph baseline from NVIDIA.
Forest fires increasingly threaten ecological integrity, human communities, and infrastructure, with vegetation composition significantly influencing fire behavior, intensity, and spatial dynamics. Accurate characterization of forest ecosystems across multiple taxonomic scales is therefore critical for effective fire prediction models, fuel load assessments, and strategic fire suppression planning. In this context, we introduce a novel hierarchical machine learning framework utilizing the National Data Platform (NDP) [1] to classify vegetation across three distinct ecological levels: Plant Functional Type (PFT), genus, and species. This hierarchical approach aims to provide precise and actionable insights for enhanced fire risk assessment and management.
SimpleScalar is a cycle-accurate simulator that has been used in computer architecture and compiler research, particularly for studying superscalar processors with out-of-order execution. Originally designed to simulate the Alpha and PISA ISAs, it has been extended to support other architectures such as PowerPC, MIPS, and ARM. This paper proposes to retarget SimpleScalar to support RISC-V architecture with RV64GC configuration, which covers a standard set of extensions (G) that are commonly used for general-purpose computing on RISC-V architectures (i.e. I, M, A, F, zifencei, zicsr), and the ISA extension for compressed instructions (C). Furthermore, a hardware optimization technique called macro-op fusion will be incorporated into SimpleScalar to improve performance by combining two or more instructions into a single, more complex instruction during the decoding stage. Preliminary performance evaluation will be conducted by executing MiBench benchmarks on the prototype implementation of SimpleScalar RV64GC.
Power capping, which is an essential component of power oversubscription, has been widely used in data centers to host more servers than allowed by the capacity of their power infrastructures, in order to avoid expensive power upgrade and reduce capital expenses. Traditionally, power capping is performed mainly with CPU frequency and voltage scaling, which cannot be directly applied to the GPU servers that are commonly deployed in today's data centers, because GPUs can have much higher power consumption than CPUs. Recently proposed GPU power capping solutions are designed for a single GPU and so cannot be used on GPU servers that have a host CPU and multiple GPUs to process machine learning (ML) workloads. Hence, a joint power capping solution must be designed to coordinate the host CPU and all the GPUs in a server for optimizing ML inference performance. In this paper, we propose CapGPU, a power capping framework for today's GPU servers that run ML workloads on multiple GPUs and a host CPU in each server. In sharp contrast to existing solutions that try to control the server power consumption by throttling either the CPU or one GPU, in a separate manner, CapGPU features a multi-input multi-output (MIMO) power control methodology and a novel weight assignment algorithm that dynamically adjusts the weights assigned to each CPU/GPU based on their measured throughput. Consequently, the overall ML inference performance can be optimized. Our hardware testbed results demonstrate that CapGPU outperforms several state-of-the-art power capping solutions by having more precise power control, higher inference throughput, and better inference latency guarantees.
