COVID-19 pandemic created unexpected demand for our physical infrastructure. We increased our computing supply by growing our infrastructure footprint as well as expanded existing capacity by using various techniques among those DVFS boosting. This paper describes our experience in deploying DVFS boosting to expand capacity.

The increasing demand for computational power has led to the creation and deployment of large-scale data centers. During the last few years, data centers have seen improvements aimed at increasing computational density - the amount of throughput that can be achieved within the allocated physical footprint. This need to pack more compute in the same physical space has led to density optimized server designs. Density optimized servers push compute density significantly beyond what can be achieved by blade servers by using innovative modular chassis based designs. This paper presents a comprehensive analysis of the impact of socket density on intra-server thermals and demonstrates that increased socket density inside the server leads to large temperature variations among sockets due to inter-socket thermal coupling. The paper shows that traditional chip-level and data center-level temperature-aware scheduling techniques do not work well for thermally-coupled sockets. The paper proposes new scheduling techniques that account for the thermals of the socket a task is scheduled on, as well as thermally coupled nearby sockets. The proposed mechanisms provide 2.5% to 6.5% performance improvements across various workloads and as much as 17% over traditional temperature-aware schedulers for computation-heavy workloads.

Autonomous driving systems have attracted a significant amount of interest recently, and many industry leaders, such as Google, Uber, Tesla, and Mobileye, have invested a large amount of capital and engineering power on developing such systems. Building autonomous driving systems is particularly challenging due to stringent performance requirements in terms of both making the safe operational decisions and finishing processing at real-time. Despite the recent advancements in technology, such systems are still largely under experimentation and architecting end-to-end autonomous driving systems remains an open research question. To investigate this question, we first present and formalize the design constraints for building an autonomous driving system in terms of performance, predictability, storage, thermal and power. We then build an end-to-end autonomous driving system using state-of-the-art award-winning algorithms to understand the design trade-offs for building such systems. In our real-system characterization, we identify three computational bottlenecks, which conventional multicore CPUs are incapable of processing under the identified design constraints. To meet these constraints, we accelerate these algorithms using three accelerator platforms including GPUs, FPGAs, and ASICs, which can reduce the tail latency of the system by 169x, 10x, and 93x respectively. With accelerator-based designs, we are able to build an end-to-end autonomous driving system that meets all the design constraints, and explore the trade-offs among performance, power and the higher accuracy enabled by higher resolution cameras.

As the power density and power consumption of large scale datacenters continue to grow, the challenges of removing heat from these datacenters and keeping them cool is an increasingly urgent and costly. With the largest datacenters now exceeding over 200 MW of power, the cooling systems that prevent overheating cost on the order of tens of millions of dollars. Prior work proposed to deploy phase change materials (PCM) and use Thermal Time Shifting (TTS) to reshape the thermal load of a datacenter by storing heat during peak hours of high utilization and releasing it during off hours when utilization is low, enabling a smaller cooling system to handle the same peak load. The peak cooling load reduction enabled by TTS is greatly beneficial, however TTS is a passive system that cannot handle many workload mixtures or adapt to changing load or environmental characteristics. In this work we propose VMT, a thermal aware job placement technique that adds an active, tunable component to enable greater control over datacenter thermal output. We propose two different job placement algorithms for VMT and perform a scale out study of VMT in a simulated server cluster. We provide analysis of the use cases and trade-offs of each algorithm, and show that VMT reduces peak cooling load by up to 12.8% to provide over two million dollars in cost savings when a smaller cooling system is installed, or allows for over 7,000 additional servers to be added in scenarios where TTS is ineffective.

Datacenters, or warehouse scale computers, are rapidly increasing in size and power consumption. However, this growth comes at the cost of an increasing thermal load that must be removed to prevent overheating and server failure. In this paper, we propose to use phase changing materials (PCM) to shape the thermal load of a datacenter, absorbing and releasing heat when it is advantageous to do so. We present and validate a methodology to study the impact of PCM on a datacenter, and evaluate two important opportunities for cost savings. We find that in a datacenter with full cooling system subscription, PCM can reduce the necessary cooling system size by up to 12% without impacting peak throughput, or increase the number of servers by up to 14.6% without increasing the cooling load. In a thermally constrained setting, PCM can increase peak throughput up to 69% while delaying the onset of thermal limits by over 3 hours.