Software developers commonly exploit multicore processors by building multithreaded software in which all threads of an application share a single address space. This shared address space has a cost: kernel virtual memory operations such as handling soft page faults, growing the address space, mapping files, etc. can limit the scalability of these applications. In widely-used operating systems, all of these operations are synchronized by a single per-process lock. This paper contributes a new design for increasing the concurrency of kernel operations on a shared address space by exploiting read-copy-update (RCU) so that soft page faults can both run in parallel with operations that mutate the same address space and avoid contending with other page faults on shared cache lines. To enable such parallelism, this paper also introduces an RCU-based binary balanced tree for storing memory mappings. An experimental evaluation using three multithreaded applications shows performance improvements on 80 cores ranging from 1.7× to 3.4× for an implementation of this design in the Linux 2.6.37 kernel. The RCU-based binary tree enables soft page faults to run at a constant cost with an increasing number of cores, suggesting that the design will scale well beyond 80 cores.

Software developers commonly exploit multicore processors by building multithreaded software in which all threads of an application share a single address space. This shared address space has a cost: kernel virtual memory operations such as handling soft page faults, growing the address space, mapping files, etc. can limit the scalability of these applications. In widely-used operating systems, all of these operations are synchronized by a single per-process lock. This paper contributes a new design for increasing the concurrency of kernel operations on a shared address space by exploiting read-copy-update (RCU) so that soft page faults can both run in parallel with operations that mutate the same address space and avoid contending with other page faults on shared cache lines. To enable such parallelism, this paper also introduces an RCU-based binary balanced tree for storing memory mappings. An experimental evaluation using three multithreaded applications shows performance improvements on 80 cores ranging from 1.7× to 3.4× for an implementation of this design in the Linux 2.6.37 kernel. The RCU-based binary tree enables soft page faults to run at a constant cost with an increasing number of cores, suggesting that the design will scale well beyond 80 cores.

Software developers commonly exploit multicore processors by building multithreaded software in which all threads of an application share a single address space. This shared address space has a cost: kernel virtual memory operations such as handling soft page faults, growing the address space, mapping files, etc. can limit the scalability of these applications. In widely-used operating systems, all of these operations are synchronized by a single per-process lock. This paper contributes a new design for increasing the concurrency of kernel operations on a shared address space by exploiting read-copy-update (RCU) so that soft page faults can both run in parallel with operations that mutate the same address space and avoid contending with other page faults on shared cache lines. To enable such parallelism, this paper also introduces an RCU-based binary balanced tree for storing memory mappings. An experimental evaluation using three multithreaded applications shows performance improvements on 80 cores ranging from 1.7× to 3.4× for an implementation of this design in the Linux 2.6.37 kernel. The RCU-based binary tree enables soft page faults to run at a constant cost with an increasing number of cores, suggesting that the design will scale well beyond 80 cores.

Today's system programmers go to great lengths to extend the languages in which they program. For instance, system-specific compilers find errors in Linux and other systems, and add support for specialized control flow to Qt and event-based programs. These compilers are difficult to build and cannot always understand each other's language changes. However, they can greatly improve code understandability and correctness, advantages that should be accessible to all programmers.We describe an extension-oriented compiler for C called xoc. An extension-oriented compiler, unlike a conventional extensible compiler, implements new features via many small extensions that are loaded together as needed. Xoc gives extension writers full control over program syntax and semantics while hiding many compiler internals. Xoc programmers concisely define powerful compiler extensions that, by construction, can be combined; even some parts of the base compiler, such as GNU C compatibility, are structured as extensions.Xoc is based on two key interfaces. Syntax patterns allow extension writers to manipulate language fragments using concrete syntax. Lazy computation of attributes allows extension writers to use the results of analyses by other extensions or the core without needing to worry about pass scheduling.Extensions built using xoc include xsparse, a 345-line extension that mimics Sparse, Linux's C front end, and xlambda, a 170-line extension that adds function expressions to C. An evaluation of xoc using these and 13 other extensions shows that xoc extensions are typically more concise than equivalent extensions written for conventional extensible compilers and that it is possible to compose extensions.

It is challenging to simultaneously achieve multicore scalability and high disk throughput in a file system. For example, even for commutative operations like creating different files in the same directory, current file systems introduce cache-line conflicts when updating an in-memory copy of the on-disk directory block, which limits scalability. ScaleFS is a novel file system design that decouples the in-memory file system from the on-disk file system using per-core operation logs. This design facilitates the use of highly concurrent data structures for the in-memory representation, which allows commutative operations to proceed without cache conflicts and hence scale perfectly. ScaleFS logs operations in a per-core log so that it can delay propagating updates to the disk representation (and the cache-line conflicts involved in doing so) until an fsync. The fsync call merges the per-core logs and applies the operations to disk. ScaleFS uses several techniques to perform the merge correctly while achieving good performance: timestamped linearization points to order updates without introducing cache-line conflicts, absorption of logged operations, and dependency tracking across operations. Experiments with a prototype of ScaleFS show that its implementation has no cache conflicts for 99% of test cases of commutative operations generated by Commuter, scales well on an 80-core machine, and provides on-disk performance that is comparable to that of Linux ext4. † Now at VMware. ‡ Now at Apple. § Now at Google.