Search

The heterogeneity of Deep Learning models, libraries, and hardware poses an important challenge for improving model inference performance. Auto-tuners address this challenge via automatic tensor program optimization towards a target-device. However, auto-tuners incur a substantial time cost to complete given their design necessitates performing tensor program candidate measurements serially within an isolated target-device to minimize latency measurement inaccuracy. In this paper we propose DOPpler, a parallel auto-tuning measurement infrastructure. DOPpler allows for considerable auto-tuning speedup over conventional approaches whilst maintaining high-quality tensor program optimization. DOPpler accelerates the auto-tuning process by proposing a parallel execution engine to efficiently execute candidate tensor programs in parallel across the CPU-host and GPU target-device, and overcomes measurement inaccuracy by introducing a high-precision on-device measurement technique when measuring tensor program kernel latency. DOPpler is designed to automatically calculate the optimal degree of parallelism to provision fast and accurate auto-tuning for different tensor programs, auto-tuners and target-devices. Experiment results show that DOPpler reduces total auto-tuning time by 50.5% on average whilst achieving optimization gains equivalent to conventional auto-tuning infrastructure.

When COVID-19 led to mandatory working from home, significant blind spots in supporting the sociality of working life—in the moment and over time—were revealed in enterprise video meetings, and these were a key factor in reports about videoconferencing fatigue. Drawing on a large study (N = 849) of one global technology company’s employees’ experiences of all-remote video meetings during the COVID-19 pandemic, we use a dialectic method to explore the tensions expressed by employees around effectiveness and sociality, as well as their strategies to cope with these tensions. We argue that videoconferencing fatigue arose partly due to work practices and technologies designed with assumptions of steady states and taken-for-granted balances between task and social dimensions of work relationships. Our analysis offers a social lens on videoconferencing fatigue and suggests the need to reconceptualize ideas around designing technologies and practices to enable both effectiveness and sociality in the context of video meetings.

When COVID-19 led to mandatory working from home, significant blind spots in supporting the sociality of working life—in the moment and over time—were revealed in enterprise video meetings, and these were a key factor in reports about videoconferencing fatigue. Drawing on a large study (N = 849) of one global technology company’s employees’ experiences of all-remote video meetings during the COVID-19 pandemic, we use a dialectic method to explore the tensions expressed by employees around effectiveness and sociality, as well as their strategies to cope with these tensions. We argue that videoconferencing fatigue arose partly due to work practices and technologies designed with assumptions of steady states and taken-for-granted balances between task and social dimensions of work relationships. Our analysis offers a social lens on videoconferencing fatigue and suggests the need to reconceptualize ideas around designing technologies and practices to enable both effectiveness and sociality in the context of video meetings.

Deep learning and artificial intelligence are often viewed as panacea technologies — ones which can decarbonise many industries. But what is the carbon cost of these systems? Damian Borowiec, Richard R. Harper and Peter Garraghan discuss.

To accelerate the training of Deep Learning (DL) models, clusters of machines equipped with hardware accelerators such as GPUs are leveraged to reduce execution time. State-of-the-art resource managers are needed to increase GPU utilization and maximize throughput. While co-locating DL jobs on the same GPU has been shown to be effective, this can incur interference causing slowdown. In this paper we propose Horus: an interference-aware and prediction-based resource manager for DL systems. Horus proactively predicts GPU utilization of heterogeneous DL jobs extrapolated from the DL model’s computation graph features, removing the need for online profiling and isolated reserved GPUs. Through micro-benchmarks and job co-location combinations across heterogeneous GPU hardware, we identify GPU utilization as a general proxy metric to determine good placement decisions, in contrast to current approaches which reserve isolated GPUs to perform online profiling and directly measure GPU utilization for each unique submitted job. Our approach promotes high resource utilization and makespan reduction; via real-world experimentation and large-scale trace driven simulation, we demonstrate that Horus outperforms other DL resource managers by up to 61.5% for GPU resource utilization, 23.7–30.7% for makespan reduction and 68.3% in job wait time reduction.

Cloud datacenters capable of provisioning high performance Machine Learning-as-a-Service (MLaaS) at reduced resource cost is achieved via auto-tuning: automated tensor program optimization of Deep Learning models to minimize inference latency within a hardware device. However given the extensive heterogeneity of Deep Learning models, libraries, and hardware devices, performing auto-tuning within Cloud datacenters incurs a significant time, compute resource, and energy cost of which state-of-the-art auto-tuning is not designed to mitigate. In this paper we propose Trimmer, a high performance and cost-efficient Deep Learning auto-tuning framework for Cloud datacenters. Trimmer maximizes DL model performance and tensor program cost-efficiency by preempting tensor program implementations exhibiting poor optimization improvement; and applying an ML-based filtering method to replace expensive low performing tensor programs to provide greater likelihood of selecting low latency tensor programs. Through an empirical study exploring the cost of DL model optimization techniques, our analysis indicates that 26-43% of total energy is expended on measuring tensor program implementations that do not positively contribute towards auto-tuning. Experiment results show that Trimmer achieves high auto-tuning cost-efficiency across different DL models, and reduces auto-tuning energy use by 21.8-40.9% for Cloud clusters whilst achieving DL model latency equivalent to state-of-the-art techniques.

To accelerate the training of Deep Learning (DL) models, clusters of machines equipped with hardware accelerators such as GPUs are leveraged to reduce execution time. State-of-the-art resource managers are needed to increase GPU utilization and maximize throughput. While co-locating DL jobs on the same GPU has been shown to be effective, this can incur interference causing slowdown. In this paper we propose Horus: an interference-aware and prediction-based resource manager for DL systems. Horus proactively predicts GPU utilization of heterogeneous DL jobs extrapolated from the DL model’s computation graph features, removing the need for online profiling and isolated reserved GPUs. Through micro-benchmarks and job co-location combinations across heterogeneous GPU hardware, we identify GPU utilization as a general proxy metric to determine good placement decisions, in contrast to current approaches which reserve isolated GPUs to perform online profiling and directly measure GPU utilization for each unique submitted job. Our approach promotes high resource utilization and makespan reduction; via real-world experimentation and large-scale trace driven simulation, we demonstrate that Horus outperforms other DL resource managers by up to 61.5% for GPU resource utilization, 23.7–30.7% for makespan reduction and 68.3% in job wait time reduction.