Linearizability is the strongest form of consistency for concurrent systems, but most large-scale storage systems settle for weaker forms of consistency. RIFL provides a general-purpose mechanism for converting at-least-once RPC semantics to exactly-once semantics, thereby making it easy to turn non-linearizable operations into linearizable ones. RIFL is designed for large-scale systems and is lightweight enough to be used in low-latency environments. RIFL handles data migration by associating linearizability metadata with objects in the underlying store and migrating metadata with the corresponding objects. It uses a lease mechanism to implement garbage collection for metadata. We have implemented RIFL in the RAMCloud storage system and used it to make basic operations such as writes and atomic increments linearizable; RIFL adds only 530 ns to the 13.5 μs base latency for durable writes. We also used RIFL to construct a new multi-object transaction mechanism in RAMCloud; RIFL's facilities significantly simplified the transaction implementation. The transaction mechanism can commit simple distributed transactions in about 20 μs and it outperforms the H-Store main-memory database system for the TPC-C benchmark.

We propose a speculative multi-threading processor architecture called Pinot. Pinot exploits parallelism over a wide range of granularities without modifying program sources. Since exploitation of fine-grain parallelism suffers from limits of parallelism and overhead incurred by parallelization, it is better to extract coarse-grain parallelism. Coarse-grain parallelism is biased in some programs (mainly, numerical ones) and some program portions. Therefore, exploiting both coarse- and fine-grain parallelism is a key to the performance of speculative multithreading. The features of Pinot are as follows: (1) A parallelizing tool extracts parallelism at any level of granularity (e.g. even ten thousand instructions) from any program sub-structures (e.g. loops, calls, or basic blocks). The tool utilizes formulation in which the parallelization process is reduced to a combinatorial optimization problem. (2) A parallel execution model with extension of thread control instructions is designed in order to minimize the increase of the dynamic instruction count. The model employs implicit thread termination and cancellation, as well as register value transfer without synchronization. (3) A versioning cache called version resolution cache (VRC) accomplishes both coarse- and fine-grained speculative multithreading. VRC operates as a large buffer for coarse-grained multi-threading. In addition, it provides low latency inter-thread communication with an update-based protocol for fine-grained multi-threading. We performed cycle-accurate simulations with 38 programs from the SPEC and MiBench benchmarks. The speedup with 4-processor-element-Pinot is up to 3.7 times, and 2.2 times on geometric mean against a conventional processor. The speedup in a program (susan) drops from 3.7 to 1.6 when the speculative buffer size is limited to 256 bytes. It confirms that exploiting coarse-grain parallelism is essential to the improved performance. FPGA implementation shows 32% overhead of area and 12% increase of critical path delay compared to a conventional processor.