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OS frontiers in the AI era
- 2. Who Am I • Fall 2020. Assoc. Prof, UVA CS – Looking forward to it! • 2014 – 2020. Asst. Prof, Purdue ECE • 2014 PhD in CS. Rice • 2008 MS + BS. Tsinghua 2
- 5. The 3rd boom of AI 1960s 1970s 1980s 1990s 2000s 2010s 2020s Classical AI Expert systems Machine learning Ups Downs 6
- 6. Two Pendulums 1960s 1970s 1980s 1990s 2000s 2010s 2020s Expert systems Machine learning Ups Downs Centralized Decentralized Dedicated hw & net PC + Internet Datacenters Cloud computing 5G, edge, embedded AI… 7 Classical AI
- 7. Two Pendulums 1960s 1970s 1980s 1990s 2000s 2010s 2020s Expert systems Machine learning Generalized Specialized Microcomputers, x86, DBMS, PC Mainframes, Apollo11 Ups Downs Wearables/IoT, NoSQL, GPU/FPGA, RISCV Centralized Decentralized Dedicated hw & net PC + Internet Datacenters Cloud computing 5G, edge, embedded AI… 8 Classical AI
- 8. Two Pendulums 1960s 1970s 1980s 1990s 2000s 2010s 2020s Expert systems Machine learning Generalized Specialized Microcomputers, x86, DBMS, PC Mainframes, Apollo11 Ups Downs Wearables/IoT, NoSQL, GPU/FPGA, RISCV Centralized Decentralized Dedicated hw & net PC + Internet Datacenters Cloud computing 5G, edge, embedded AI… 9 Classical AI
- 10. What you have learnt from undergrad OS class 1960s 1970s 1980s 1990s 2000s 2010s 2020s GOFAI Expert systems Machine learning Generalized Specialized Microcomputers, x86, DBMS, PC Mainframes, Apollo11 Ups Downs Wearables/IoT, NoSQL, GPU/FPGA, RISCV Centralized Decentraliz ed Dedicated hw & net PC + Internet Datacenters Cloud computing 5G, edge, embedded AI… 14
- 11. What OS people are working on 1960s 1970s 1980s 1990s 2000s 2010s 2020s GOFAI Expert systems Machine learning Generalized Specialized Microcomputers, x86, DBMS, PC Mainframes, Apollo11 Ups Downs Wearables/IoT, NoSQL, GPU/FPGA, RISCV Centralized Decentraliz ed Dedicated hw & net PC + Internet Datacenters Cloud computing 5G, edge, embedded AI… 15
- 13. • Diverse • A few common OSes • Many specialized ones • Serving “things” in addition to humans • Defined by scenarios • Generic OSes firmware • Specialized OSes overlays • Blurry boundaries • arch, runtime, compiler, kernel, hypervisor, trusted exec environment… Cloud Edge Devices 18
- 17. Stream analytics: state of the art • Classic engines? • StreamBase, Aurora, TelegraphCQ, NiagaraST… • Single threaded. Not scaling well. • Modern engines for datacenters? • Apache Flink, Spark Streaming, Beam… • Designed for tens ‐ hundreds of machines. Scaling out. • Assuming okay if individual nodes perform poorly • As analytics moves to the edge bad 23
- 18. Project StreamBox stream analytics at the memory speed 24 • RDMA • Co‐designed with mm/scheduling • RDMA • Co‐designed with mm/scheduling Stream pipeline Threads Ingestion Scheduler Mem • Squeeze parallelism for multi/manycore • Manage NUMA domains • Squeeze parallelism for multi/manycore • Manage NUMA domains Exploit high‐bandwidth memoryExploit high‐bandwidth memory [ASPLOS'19] "StreamBox‐HBM: Stream Analytics on High Bandwidth Hybrid Memory," Hongyu Miao, Myeongjae Jeon, Gennady Pekhimenko, Kathryn S. McKinley, and Felix Xiaozhu Lin [USENIX ATC'17] "StreamBox: Modern Stream Processing on a Multicore Machine," Hongyu Miao, Heejin Park, Myeongjae Jeon, Gennady Pekhimenko, Kathryn S. McKinley, and Felix Xiaozhu Lin, in Proc. USENIX Annual Technical Conference, 2017. [ASPLOS'16] "memif: Towards Programming Heterogeneous Memory Asynchronously," Felix Xiaozhu Lin and Xu Liu, in Proc. ACM Int. Conf. Architectural Support for Programming Languages and Operating Systems, 2016.
- 19. CoresCores High‐bandwidth hybrid memory 25 3D DRAM Normal DRAM Tradeoffs: capacity vs. bandwidth Untraditional memory hierarchy No latency benefit Unlike SRAM+DRAM 16 GB 375 GB/s ~100 GB ~100 GB/s
- 20. 26 Already on off‐the‐shelf machinesIntel Xeon Phi Knights Landing (KNL)
- 23. Case 2: Autonomous AI on Cameras [MobiSys'20] "Approximate Query Processing on Autonomous Cameras," Mengwei Xu, Xiwen Zhang, Yunxin Liu, Xuanzhe Liu, and Felix Xiaozhu Lin 29
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- 27. 33 Cut the cords
- 32. Query: (car, 30 mins) Install 7:00AM-7:30AM [500 + 100] Cars 7:30AM-8:00AM [700 + 140] Cars 8:00AM-8:30AM [800 + 180] Cars 8:30AM-9:00AM [400 + 100] Cars 9:30AM-10:00AM [200 + 80] Cars Sample & capture 200‐80 +80 Elf: Query model 38
- 33. Camera Operating System Planning Energy via Reinforcement Learning Planning Energy via Reinforcement Learning Sampled frames Aggregator with error Integration Aggregator with error Integration Selected NeuralNet Object counts Elf: System Internals 39
- 35. 7:00AM-7:30AM [500 + 100] Cars 7:30AM-8:00AM [700 + 140] Cars 8:00AM-8:30AM [800 + 180] Cars 8:30AM-9:00AM [400 + 100] Cars 9:30AM-10:00AM [200 + 80] Cars Ground Truth Error: 11% Confidence interval width: 17% Auburn, AL Hampton, NY Jackson, WY Taipei Taipei ~1000 hours 41
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- 40. 46 CPU Co Proc 2.5GHz 50MHz DRAM IO A heterogeneous SoC
- 41. 47 CPU Co Proc 2.5GHz 50MHz DRAM IO Weak co‐processors: suits low‐power IO tasks! high efficiency Linux Kernel IO tasks
- 42. 48 CPU Co Proc 2.5GHz 50MHz DRAM IO Kernel execution on weak co‐processors? Linux Kernel IO tasks Diff ISA No MMU …
- 43. 49 CPU Co Proc DRAM IO Co‐processor translates unmodified kernel binary Dynamic Binary Translation Linux Kernel IO tasks [USENIX ATC 19] "Transkernel: An Executor for Commodity Kernels on Peripheral Cores," Liwei Guo, Shuang Zhai, Yi Qiao, and Felix Xiaozhu Lin
- 45. Recap • What are OSes in 2020? • Three OS projects: • spanning IoT, mobile, and datacenters • each with different flavors • The builder culture • open blackboxes • break things • build things from the ground up • Started by a small group of hardcore hackers • Now more diverse and inclusive • A brave new world 51 StreamBox Elf