Prioritized Garbage Collection: Explicit GC Support for Software Caches

Programmers routinely trade space for time to increase performance, often in the form of caching or memoization. In managed languages like Java or JavaScript, however, this space-time tradeoff is complex. Using more space translates into higher garbage collection costs, especially at the limit of available memory. Existing runtime systems provide limited support for space-sensitive algorithms, forcing programmers into difficult and often brittle choices about provisioning. This paper presents prioritized garbage collection, a cooperative programming language and runtime solution to this problem. Prioritized GC provides an interface similar to soft references, called priority references, which identify objects that the collector can reclaim eagerly if necessary. The key difference is an API for defining the policy that governs when priority references are cleared and in what order. Application code specifies a priority value for each reference and a target memory bound. The collector reclaims references, lowest priority first, until the total memory footprint of the cache fits within the bound. We use this API to implement a space-aware least-recently-used (LRU) cache, called a Sache, that is a drop-in replacement for existing caches, such as Google's Guava library. The garbage collector automatically grows and shrinks the Sache in response to available memory and workload with minimal provisioning information from the programmer. Using a Sache, it is almost impossible for an application to experience a memory leak, memory pressure, or an out-of-memory crash caused by software caching.Comment: to appear in OOPSLA 201

Detecting errors with configurable whole-program dataflow analysis

In this paper, we present an automatic compiler-based approach for detecting programming errors. Our system uses a configurable and scalable whole-program dataflow analysis engine driven by highlevel programmer-written annotations. We show that our system can automatically detect a wide range of programmer errors in C programs, including improper use of libraries, information leaks, and security vulnerabilities. We show that the aggressive compiler analysis that our system performs yields precise results. Further, our system detects a wide range of errors with greater scalability than previous automatic approaches. For one important class of security vulnerabilities, our system automatically finds all known errors in five medium to large C programs without producing any false positives. 1

Abstract An Annotation Language for Optimizing Software Libraries

This paper introduces an annotation language and a compiler that together can customize a library implementation for specific application needs. Our approach is distinguished by its ability to exploit high level, domain-specific information in the customization process. In particular, the annotations provide semantic information that enables our compiler to analyze and optimize library operations as if they were primitives of a domain-specific language. Thus, our approach yields many of the performance benefits of domain-specific languages, without the effort of developing a new compiler for each domain. This paper presents the annotation language, describes its role in optimization, and illustrates the benefits of the overall approach. Using a partially implemented compiler, we show how our system can significantly improve the performance of two applications written using the PLAPACK parallel linear algebra library.

An Annotation Language for Optimizing Software Libraries

This paper introduces an annotation language and a compiler that together can customize a library implementation for specific application needs. Our approach is distinguished by its ability to exploit high level, domain-specific information in the customization process. In particular, the annotations provide semantic information that enables our compiler to analyze and optimize library operations as if they were primitives of a domain-specific language. Thus, our approach yields many of the performance benefits of domain-specific languages, without the effort of developing a new compiler for each domain. This paper presents the annotation language, describes its role in optimization, and illustrates the benefits of the overall approach. Using a partially implemented compiler, we show how our system can significantly improve the performance of two applications written using the PLAPACK parallel linear algebra library. 1 Introduction Software libraries are a common mechanism for re-us..

Bytecode Verification for Haskell

In this paper we present a method for verifying Yhc bytecode, an intermediate form of Haskell suitable for mobile code applications. We examine the issues involved with verifying Yhc bytecode programs, and we present a proof-of-concept bytecode compiler and verifier. Verification is a static analysis which ensures that a bytecode program is type-safe. The ability to check type-safety is important for mobile code situations where untrusted code may be executed. Type-safety subsumes the critical memory-safety property and excludes important classes of exploitable bugs, such as buffer overflows. Haskell’s rich type system also allows programmers to build effective, static security policies and enforce them using the type checker. Verification allows us to be confident the constraints of our security policy are enforced.

Error checking with client-driven pointer analysis

This paper presents a new client-driven pointer analysis algorithm that automatically adjusts its precision in response to the needs of client analyses. Using five significant error detection problems as clients, we evaluate our algorithm on 18 real C programs. We compare the accuracy and performance of our algorithm against several commonly-used fixedprecision algorithms. We find that the client-driven approach effectively balances cost and precision, often producing results as accurate as fixed-precision algorithms that are many times more costly. Our algorithm works because many client problems only need a small amount of extra precision applied to selected portions of each input program.