Search for: All records

Recent innovation in large language models (LLMs), and their myriad use cases have rapidly driven up the compute demand for datacenter GPUs. Several cloud providers and other enterprises plan to substantially grow their datacenter capacity to support these new workloads. A key bottleneck resource in datacenters is power, which LLMs are quickly saturating due to their rapidly increasing model sizes.We extensively characterize the power consumption patterns of a variety of LLMs and their configurations. We identify the differences between the training and inference power consumption patterns. Based on our analysis, we claim that the average and peak power utilization in LLM inference clusters should not be very high. Our deductions align with data from production LLM clusters, revealing that inference workloads offer substantial headroom for power oversubscription. However, the stringent set of telemetry and controls that GPUs offer in a virtualized environment make it challenging to build a reliable and robust power management framework.We leverage the insights from our characterization to identify opportunities for better power management. As a detailed use case, we propose a new framework called POLCA, which enables power oversubscription in LLM inference clouds. POLCA is robust, reliable, and readily deployable. Using open-source models to replicate the power patterns observed in production, we simulate POLCA and demonstrate that we can deploy 30% more servers in existing clusters with minimal performance loss. 

Kernel bypass systems have demonstrated order of magnitude improvements in throughput and tail latency for network-intensive applications relative to traditional operating systems (OSes). To achieve such excellent performance, however, they rely on dedicated resources (e.g., spinning cores, pinned memory) and require application rewriting. This is unattractive to cloud operators because they aim to densely pack applications, and rewriting cloud software requires a massive investment of valuable developer time. For both reasons, kernel bypass, as it exists, is impractical for the cloud. In this paper, we show these compromises are not necessary to unlock the full benefits of kernel bypass. We present Junction, the first kernel bypass system that can pack thousands of instances on a machine while providing compatibility with unmodified Linux applications. Junction achieves high density through several advanced NIC features that reduce pinned memory and the overhead of monitoring large numbers of queues. It maintains compatibility with minimal overhead through optimizations that exploit a shared address space with the application. Junction scales to 19–62× more instances than existing kernel bypass systems and can achieve similar or better performance without code changes. Furthermore, Junction delivers significant performance benefits to applications previously unsupported by kernel bypass, including those that depend on runtime systems like Go, Java, Node, and Python. In a comparison to native Linux, Junction increases throughput by 1.6–7.0× while using 1.2–3.8× less cores across seven applications. 

To mitigate climate change, we must reduce carbon emissions from hyperscale cloud computing. We find that cloud compute servers cause the majority of emissions in a general-purpose cloud. Thus, we motivate designing carbon-efficient compute server SKUs, or GreenSKUs, using recently-available low-carbon server components. To this end, we design and build three GreenSKUs using low-carbon components, such as energy-efficient CPUs, reused old DRAM via CXL, and reused old SSDs. We detail several challenges that limit GreenSKUs, carbon savings at scale and may prevent their adoption by cloud providers. To address these challenges, we develop a novel methodology and associated framework, GSF (GreenSKU Framework), that enables a cloud provider to systematically evaluate a GreenSKU’s carbon savings at scale. We implement GSF within Microsoft Azure’s production constraints to evaluate our three GreenSKUs’ carbon savings. Using GSF, we show that our most carbon-efficient GreenSKU reduces emissions per core by 28% compared to currently-deployed cloud servers. When designing GreenSKUs to meet applications’ performance requirements, we reduce emissions by 15%. When incorporating overall data center overheads, our GreenSKU reduces Azure’s net cloud emissions by 8%. 

As modern server GPUs are increasingly power intensive, better power management mechanisms can significantly reduce the power consumption, capital costs, and carbon emissions in large cloud datacenters. This letter uses diverse datacenter workloads to study the power management capabilities of modern GPUs. We find that current GPU management mechanisms have limited compatibility and monitoring support under cloud virtualization. They have sub-optimal, imprecise, and non-intuitive implementations of Dynamic Voltage and Frequency Scaling (DVFS) and power capping. Consequently, efficient GPU power management is not widely deployed in clouds today. To address these issues, we make actionable recommendations for GPU vendors and researchers.