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To represent the entire carbon footprint of computing devices, carbon metrics often include both an embodied cost (i.e., carbon cost to produce the device) and an operational cost (i.e., carbon cost to run the device). The embodied carbon cost is typically high, but it is amortized over the lifetime of the device. In this vision statement, we argue that for carbon metrics to be useful, we need (i) accurate metrics for lifetime, which are challenging for SSDs, and (ii) correct reasoning about carbon costs when using such metrics. 

GPU sharing between workloads is an e!ective approach to increase GPU utilization and reduce idle power waste. To minimize resource contention under GPU sharing, current architectures allow users to allocate core GPU compute resources exclusively to workloads. However, identifying the most e''cient GPU compute resource allocation for colocated workloads is challenging, as it requires balancing potential performance degradation and power savings. This paper presents a framework for finding the most energy-e''cient compute allocation for colocated workload pairs under NVIDIA MPS using lightweight prediction models. Experimental results, using a range of training, inference, and general CUDA workloads, demonstrate that our solution outperforms the equal sharing strategy by 35%, on average, and is within 1.5% of the o#ine optimal strategy. 

Identifying knee and elbow points in performance curves is a critical task in various domains, including machine learning and system design. These points represent optimal trade-offs between cost and performance, facilitating efficient decision-making and resource allocation. However, accurately determining the knees and elbows in curves poses a significant challenge. To address this challenge, we introduce Kneeliverse, an open-source library dedicated to knee/elbow point detection. Kneeliverse incorporates a suite of well-established knee-detection algorithms, including Menger, L-method, Kneedle, and DFDT. Additionally, Kneeliverse extends these algorithms to detect multiple knees and elbows in complex curves, employing a recursive approach. Kneeliverse further includes Z-Method, a recently developed algorithm specifically designed for multi-knee detection.