ASPLOS

Multi-stage serverless applications, i.e., workflows with many computation and I/O stages, are becoming increasingly representative of FaaS platforms. Despite their advantages in terms of fine-grained scalability and modular development, these ...

Classical computing plays a critical role in the advancement of quantum frontiers in the NISQ era. In this spirit, this work uses classical simulation to bootstrap Variational Quantum Algorithms (VQAs). VQAs rely upon the iterative optimization of a ...

Key-Value Stores (KVS) are foundational infrastructure components for online services. Due to their latency-critical nature, today’s best-performing KVS contain a plethora of full-stack optimizations commonly targeting read-mostly, popularity-skewed ...

Graph pattern mining (GPM) is an important application that identifies structures from graphs. Despite the recent progress, the performance gap between the state-of-the-art GPM systems and an efficient algorithm—pattern decomposition—is still at least ...

A common approach to improving resource utilization in data centers is to adaptively provision resources based on the actual workload. One fundamental challenge of doing this in microservice management frameworks, however, is that different components ...

In concurrent graph processing, different queries are evaluated on the same graph simultaneously, sharing the graph accesses via the memory hierarchy. However, different queries may traverse the graph differently, especially for those starting from ...

Large deep learning models have shown great potential with state-of-the-art results in many tasks. However, running these large models is quite challenging on an accelerator (GPU or TPU) because the on-device memory is too limited for the size of ...

Dynamic Binary Translation (DBT) is a powerful approach to support cross-architecture emulation of unmodified binaries. However, DBT systems face correctness and performance challenges, when emulating concurrent binaries from strong to weak memory ...

Memory buffer allocation for on-chip memories is a major challenge in modern machine learning systems that target ML accelerators. In interactive systems such as mobile phones, it is on the critical path of launching ML-enabled applications. In data ...