CXLMemUring: A Hardware Software Co-design Paradigm for Asynchronous and Flexible Parallel CXL Memory Pool Access

CXL has been the emerging technology for expanding memory for both the host CPU and device accelerators with load/store interface. Extending memory coherency to the PCIe root complex makes the codesign more flexible in that you can access the memory with coherency using your near-device computability. Since the capacity demand with tolerable latency and bandwidth is growing, we need to come up with a new hardware-software codesign way to offload the synthesized memory operations to the CXL endpoint, CXL switch or near CXL root complex cores like Intel DSA to fetch data; the CPU or accelerators can calculate other stuff in the backend. On CXL done loading, the data will be put into L1 if capacity fits, and the in-core ROB will be notified by mailbox and resume the calculation on the previous hardware context. Since the distance(timing window) of the load instruction sequence is unknown, a profiling-guided way of codegening and adaptively updating offloaded code will be required for a long-running job. We propose to evaluate CXLMemUring the modified BOOMv3 with added in-core-logic and CXL endpoint access simulation using CHI, and we will add a weaker RISCV Core near endpoint for code offloading, and the codegening will be based on program analysis with traditional profiling guided way

A Study of AI Population Dynamics with Million-agent Reinforcement Learning

We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning. Our intention is to put intelligent agents into a simulated natural context and verify if the principles developed in the real world could also be used in understanding an artificially-created intelligent population. To achieve this, we simulate a large-scale predator-prey world, where the laws of the world are designed by only the findings or logical equivalence that have been discovered in nature. We endow the agents with the intelligence based on deep reinforcement learning (DRL). In order to scale the population size up to millions agents, a large-scale DRL training platform with redesigned experience buffer is proposed. Our results show that the population dynamics of AI agents, driven only by each agent's individual self-interest, reveals an ordered pattern that is similar to the Lotka-Volterra model studied in population biology. We further discover the emergent behaviors of collective adaptations in studying how the agents' grouping behaviors will change with the environmental resources. Both of the two findings could be explained by the self-organization theory in nature.Comment: Full version of the paper presented at AAMAS 2018 (International Conference on Autonomous Agents and Multiagent Systems