Global Heap Research Articles

Go with the flow: compositional abstractions for concurrent data structures

Concurrent separation logics have helped to significantly simplify correctness proofs for concurrent data structures. However, a recurring problem in such proofs is that data structure abstractions that work well in the sequential setting are much harder to reason about in a concurrent setting due to complex sharing and overlays. To solve this problem, we propose a novel approach to abstracting regions in the heap by encoding the data structure invariant into a local condition on each individual node. This condition may depend on a quantity associated with the node that is computed as a fixpoint over the entire heap graph. We refer to this quantity as a flow . Flows can encode both structural properties of the heap (e.g. the reachable nodes from the root form a tree) as well as data invariants (e.g. sortedness). We then introduce the notion of a flow interface , which expresses the relies and guarantees that a heap region imposes on its context to maintain the local flow invariant with respect to the global heap. Our main technical result is that this notion leads to a new semantic model of separation logic. In this model, flow interfaces provide a general abstraction mechanism for describing complex data structures. This abstraction mechanism admits proof rules that generalize over a wide variety of data structures. To demonstrate the versatility of our approach, we show how to extend the logic RGSep with flow interfaces. We have used this new logic to prove linearizability and memory safety of nontrivial concurrent data structures. In particular, we obtain parametric linearizability proofs for concurrent dictionary algorithms that abstract from the details of the underlying data structure representation. These proofs cannot be easily expressed using the abstraction mechanisms provided by existing separation logics.

SC-Haskell

A core, but often neglected, aspect of a programming language design is its memory (consistency) model. Sequential consistency~(SC) is the most intuitive memory model for programmers as it guarantees sequential composition of instructions and provides a simple abstraction of shared memory as a single global store with atomic read and writes. Unfortunately, SC is widely considered to be impractical due to its associated performance overheads. Perhaps contrary to popular opinion, this paper demonstrates that SC is achievable with acceptable performance overheads for mainstream languages that minimize mutable shared heap. In particular, we modify the Glasgow Haskell Compiler to insert fences on all writes to shared mutable memory accessed in nonfunctional parts of the program. For a benchmark suite containing 1,279 programs, SC adds a geomean overhead of less than 0.4\% on an x86 machine. The efficiency of SC arises primarily due to the isolation provided by the Haskell type system between purely functional and thread-local imperative computations on the one hand, and imperative computations on the global heap on the other. We show how to use new programming idioms to further reduce the SC overhead; these create a virtuous cycle of less overhead and even stronger semantic guarantees (static data-race freedom).

Patterns of granular convection and separation in narrow vibration bed

Granular convection/separation of single and binary component particles are studied in a narrow vibration bed, respectively. With filling the single light particles (molecular sieve beads), the bed exhibits five different states successively by increasing the vibration frequency f from 15Hz to 70 Hz (vibration strength Γ>3), as the global convection, symmetrical heap, unsymmetrical heap, local convection and pseudo solid. Comparatively, the granular bed of the single heavy particles (steel beads) is only in pseudo solid state at the above vibration condition. By filling binary component particles (molecular sieve and same size steel beads) instead of the single component, the bed shows similar convection state with that of the single molecular sieve beads, and the heavy steel beads are aggregated in the centre of convention roll as a core. Varying the initial distribution of binary component particles, the final convection and separation are not influenced, although the aggregation process of steel beads changes.

Influence of bottom boundary condition on heat distribution within oxidizing heaps

Different types of boundary conditions for temperature (Dirichlet, Neumann, mixed and some others) at the base of a heap comprising oxidizable material are numerically examined for their influence on temperature distribution and overall heat content within the heap. We demonstrate that some global heap characteristics (for example, the total heat content) essentially depend on boundary conditions and corresponding temperature distributions within the heap, whereas other global characteristics (for example, the overall oxidation rate) are not affected significantly. Practical recommendations on selection of boundary conditions are presented.

A specification-based approach to reasoning about pointers

This paper explains how a uniform, specification-based approach to reasoning about component-based programs can be used to reason about programs that manipulate pointers. No special axioms, language semantics, global heap model, or proof rules for pointers are necessary. We show how this is possible by capturing pointers and operations that manipulate them in the specification of a software component. The proposed approach is mechanizable as long as programmers are able to understand mathematical specifications and write assertions, such as loop invariants. While some of the previous efforts in reasoning do not require such mathematical sophistication on the part of programmers, they are limited in the kinds of properties they can prove about programs that use pointers. We illustrate the idea using a "Splice" operation for linked lists, which has been used previously to explain other analysis techniques. Not only can the proposed approach be used to establish shape properties given lightweight specifications, but also it can be used to establish total correctness given more complete specifications.

Inferring Object Invariants: Extended Abstract

The program state for object-oriented languages, such as Java or C#, consists of both variables local to procedures and variables stored in the global heap. The variables stored in the heap are the fields of objects (i.e., fields of class instances). This paper proposes a technique for inferring properties of such object-oriented programs. The technique divides the work into two interacting parts: a flow-sensitive analysis that tracks the local and global state as seen at each particular program point, and a flow-insensitive analysis that tracks properties that are common among all “valid” instances of a class, independent of the program counter. The analysis is sound and works in the presence of many object references (aliasing). For precision, the analysis makes use of a recent methodology for object invariants, which makes explicit when each object's invariant is supposed to hold (valid objects) or is allowed to be violated.

Hoard

Parallel, multithreaded C and C++ programs such as web servers, database managers, news servers, and scientific applications are becoming increasingly prevalent. For these applications, the memory allocator is often a bottleneck that severely limits program performance and scalability on multiprocessor systems. Previous allocators suffer from problems that include poor performance and scalability, and heap organizations that introduce false sharing. Worse, many allocators exhibit a dramatic increase in memory consumption when confronted with a producer-consumer pattern of object allocation and freeing. This increase in memory consumption can range from a factor of P (the number of processors) to unbounded memory consumption.This paper introduces Hoard, a fast, highly scalable allocator that largely avoids false sharing and is memory efficient. Hoard is the first allocator to simultaneously solve the above problems. Hoard combines one global heap and per-processor heaps with a novel discipline that provably bounds memory consumption and has very low synchronization costs in the common case. Our results on eleven programs demonstrate that Hoard yields low average fragmentation and improves overall program performance over the standard Solaris allocator by up to a factor of 60 on 14 processors, and up to a factor of 18 over the next best allocator we tested.

Hoard

Parallel, multithreaded C and C++ programs such as web servers, database managers, news servers, and scientific applications are becoming increasingly prevalent. For these applications, the memory allocator is often a bottleneck that severely limits program performance and scalability on multiprocessor systems. Previous allocators suffer from problems that include poor performance and scalability, and heap organizations that introduce false sharing. Worse, many allocators exhibit a dramatic increase in memory consumption when confronted with a producer-consumer pattern of object allocation and freeing. This increase in memory consumption can range from a factor of P (the number of processors) to unbounded memory consumption.This paper introduces Hoard, a fast, highly scalable allocator that largely avoids false sharing and is memory efficient. Hoard is the first allocator to simultaneously solve the above problems. Hoard combines one global heap and per-processor heaps with a novel discipline that provably bounds memory consumption and has very low synchronization costs in the common case. Our results on eleven programs demonstrate that Hoard yields low average fragmentation and improves overall program performance over the standard Solaris allocator by up to a factor of 60 on 14 processors, and up to a factor of 18 over the next best allocator we tested.

Hoard

Parallel, multithreaded C and C++ programs such as web servers, database managers, news servers, and scientific applications are becoming increasingly prevalent. For these applications, the memory allocator is often a bottleneck that severely limits program performance and scalability on multiprocessor systems. Previous allocators suffer from problems that include poor performance and scalability, and heap organizations that introduce false sharing. Worse, many allocators exhibit a dramatic increase in memory consumption when confronted with a producer-consumer pattern of object allocation and freeing. This increase in memory consumption can range from a factor of P (the number of processors) to unbounded memory consumption.This paper introduces Hoard, a fast, highly scalable allocator that largely avoids false sharing and is memory efficient. Hoard is the first allocator to simultaneously solve the above problems. Hoard combines one global heap and per-processor heaps with a novel discipline that provably bounds memory consumption and has very low synchronization costs in the common case. Our results on eleven programs demonstrate that Hoard yields low average fragmentation and improves overall program performance over the standard Solaris allocator by up to a factor of 60 on 14 processors, and up to a factor of 18 over the next best allocator we tested.