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TEGRA - Scaling Up Graph Processing with Disaggregated Computing
DescriptionGraph processing workloads continue to grow in scale and complexity, demanding architectures that can adapt to diverse compute and memory requirements. Traditional scale-out accelerators couple compute and memory resources, resulting in resource underutilization when executing workloads with varying compute-to-memory intensities. In this paper, we present TEGRA, a composable, scale-up architecture for large-scale graph processing. TEGRA leverages disaggregated memory via CXL and a message-passing communication model to decouple compute and memory, enabling independent scaling of each. Through detailed evaluation using the gem5 simulator, we show that TEGRA improves memory bandwidth utilization by up to 15\% over state-of-the-art accelerators by dynamically provisioning compute based on workload demands. Our results demonstrate that TEGRA provides a flexible and efficient foundation for supporting emerging graph analytics workloads across a wide range of arithmetic intensities.