The commercial release of byte-addressable persistent memories, such as Intel/Micron 3D XPoint memory, is imminent. Ongoing research has sought mechanisms to allow programmers to implement recoverable data structures in these new main memories. Ensuring recoverability requires programmer control of the order of persistent stores; recent work proposes persistency models as an extension to memory consistency to specify such ordering. Prior work has considered persistency models at the abstraction of the instruction set architecture. Instead, we argue for extending the language-level memory model to provide guarantees on the order of persistent writes. We explore a taxonomy of guarantees a language-level persistency model might provide, considering both atomicity and ordering constraints on groups of persistent stores. Then, we propose and evaluate Acquire-Release Persistency (ARP), a language-level persistency model for C++11. We describe how to compile code written for ARP to a state-of-the-art ISA-level persistency model. We then consider enhancements to the ISA-level persistency model that can distinguish memory consistency constraints required for proper synchronization but unnecessary for correct recovery. With these optimizations, we show that ARP increases performance by up to 33.2% (19.8% avg.) over coding directly to the baseline ISA-level persistency model for a suite of persistent-write-intensive workloads. CCS CONCEPTS • Computer systems organization → Architectures; • Software and its engineering → Software notations and tools;

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Today's cloud storage stack is extremely resource hungry, burning 10-20% of datacenter x86 cores, a major "storage tax" that cloud providers must pay. Yet, the complex cloud storage stack is not completely offload-ready to today's IO accelerators. We present LeapIO, a new cloud storage stack that leverages ARM-based co-processors to offload complex storage services. LeapIO addresses many deployment challenges, such as hardware fungibility, software portability, virtualizability, composability, and efficiency. It uses a set of OS/software techniques and new hardware properties that provide a uniform address space across the x86 and ARM cores and expose virtual NVMe storage to unmodified guest VMs, at a performance that is competitive with bare-metal servers. CCS Concepts. • Computer systems organization → Cloud computing; Client-server architectures; System on a chip; Real-time system architecture.

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