- General Chair:
- Tor M. Aamodt,
- Program Chairs:
- Natalie Enright Jerger,
- Michael Swift
It is our pleasure to introduce Volume I of ASPLOS ’23. For the first time, ASPLOS has embarked on a new multi-deadline review model. ASPLOS ’23 features 3 deadlines spaced throughout the year and papers will be published in three volumes. Multiple deadlines are meant to encourage authors to submit their papers when ready and to facilitate the selection of some papers for revision. For this volume of ASPLOS ’23, we continued the 2-page extended abstract submissions that were used in ASPLOS ’21 and ASPLOS ’22. We also experimented with a new submission format, where authors were given additional pages but limited to 8000 words in an effort to improve paper readability. In our preface to Volume III, we will give a more detailed rundown of how the process worked.
Proceeding Downloads
AQUATOPE: QoS-and-Uncertainty-Aware Resource Management for Multi-stage Serverless Workflows
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 ...
CAFQA: A Classical Simulation Bootstrap for Variational Quantum Algorithms
- Gokul Subramanian Ravi,
- Pranav Gokhale,
- Yi Ding,
- William Kirby,
- Kaitlin Smith,
- Jonathan M. Baker,
- Peter J. Love,
- Henry Hoffmann,
- Kenneth R. Brown,
- Frederic T. Chong
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 ...
Cooperative Concurrency Control for Write-Intensive Key-Value Workloads
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 ...
DecoMine: A Compilation-Based Graph Pattern Mining System with Pattern Decomposition
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 ...
Erms: Efficient Resource Management for Shared Microservices with SLA Guarantees
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 ...
Glign: Taming Misaligned Graph Traversals in Concurrent Graph Processing
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 ...
Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models
- Shibo Wang,
- Jinliang Wei,
- Amit Sabne,
- Andy Davis,
- Berkin Ilbeyi,
- Blake Hechtman,
- Dehao Chen,
- Karthik Srinivasa Murthy,
- Marcello Maggioni,
- Qiao Zhang,
- Sameer Kumar,
- Tongfei Guo,
- Yuanzhong Xu,
- Zongwei Zhou
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 ...
Risotto: A Dynamic Binary Translator for Weak Memory Model Architectures
- Redha Gouicem,
- Dennis Sprokholt,
- Jasper Ruehl,
- Rodrigo C. O. Rocha,
- Tom Spink,
- Soham Chakraborty,
- Pramod Bhatotia
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 ...
TelaMalloc: Efficient On-Chip Memory Allocation for Production Machine Learning Accelerators
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 ...
Index Terms
- Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 1
Recommendations
Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| ASPLOS '19 | 351 | 74 | 21% |
| ASPLOS '18 | 319 | 56 | 18% |
| ASPLOS '17 | 320 | 53 | 17% |
| ASPLOS '16 | 232 | 53 | 23% |
| ASPLOS '15 | 287 | 48 | 17% |
| ASPLOS '14 | 217 | 49 | 23% |
| ASPLOS XV | 181 | 32 | 18% |
| ASPLOS XIII | 127 | 31 | 24% |
| ASPLOS XII | 158 | 38 | 24% |
| ASPLOS X | 175 | 24 | 14% |
| ASPLOS IX | 114 | 24 | 21% |
| ASPLOS VIII | 123 | 28 | 23% |
| ASPLOS VII | 109 | 25 | 23% |
| Overall | 2,713 | 535 | 20% |