37 research outputs found
Data representation synthesis
We consider the problem of specifying combinations of data structures with complex sharing in a manner that is both declarative and results in provably correct code. In our approach, abstract data types are specified using relational algebra and functional dependencies. We describe a language of decompositions that permit the user to specify different concrete representations for relations, and show that operations on concrete representations soundly implement their relational specification. It is easy to incorporate data representations synthesized by our compiler into existing systems, leading to code that is simpler, correct by construction, and comparable in performance to the code it replaces
Concurrent data representation synthesis
We describe an approach for synthesizing data representations for concurrent programs. Our compiler takes as input a program written using concurrent relations and synthesizes a representation of the relations as sets of cooperating data structures as well as the placement and acquisition of locks to synchronize concurrent access to those data structures. The resulting code is correct by construction: individual relational operations are implemented correctly and the aggregate set of operations is serializable and deadlock free. The relational specification also permits a high-level optimizer to choose the best performing of many possible legal data representations and locking strategies, which we demonstrate with an experiment autotuning a graph benchmark
Deductive Optimization of Relational Data Storage
Optimizing the physical data storage and retrieval of data are two key
database management problems. In this paper, we propose a language that can
express a wide range of physical database layouts, going well beyond the row-
and column-based methods that are widely used in database management systems.
We use deductive synthesis to turn a high-level relational representation of a
database query into a highly optimized low-level implementation which operates
on a specialized layout of the dataset. We build a compiler for this language
and conduct experiments using a popular database benchmark, which shows that
the performance of these specialized queries is competitive with a
state-of-the-art in memory compiled database system
Efficient Synthesis of Network Updates
Software-defined networking (SDN) is revolutionizing the networking industry,
but current SDN programming platforms do not provide automated mechanisms for
updating global configurations on the fly. Implementing updates by hand is
challenging for SDN programmers because networks are distributed systems with
hundreds or thousands of interacting nodes. Even if initial and final
configurations are correct, naively updating individual nodes can lead to
incorrect transient behaviors, including loops, black holes, and access control
violations. This paper presents an approach for automatically synthesizing
updates that are guaranteed to preserve specified properties. We formalize
network updates as a distributed programming problem and develop a synthesis
algorithm based on counterexample-guided search and incremental model checking.
We describe a prototype implementation, and present results from experiments on
real-world topologies and properties demonstrating that our tool scales to
updates involving over one-thousand nodes
Towards a software product line of trie-based collections
Collection data structures in standard libraries of programming languages are designed to excel for the average case by carefully balancing memory footprint and runtime performance. These implicit design decisions and hard-coded trade-offs do constrain users from using an optimal variant for a given problem. Although a wide range of specialized collections is available for the Java Virtual Machine (JVM), they introduce yet another dependency and complicate user adoption by requiring specific Application Program Interfaces (APIs) incompatible with the standard library. A product line for collection data structures would relieve library designers from optimizing for the general case. Furthermore, a product line allows evolving the potentially large code base of a collection family efficiently. The challenge is to find a small core framework for collection data structures which covers all variations without exhaustively listing them, while supporting good performance at the same time. We claim that the concept of Array Mapped Tries (AMTs) embodies a high degree of commonality in the sub-domain of immutable collection data structures. AMTs are flexible enough to cover most of the variability, while minimizing code bloat in the generator and the generated code. We implemented a Data Structure Code Generator (DSCG) that emits immutable collections based on an AMT skeleton foundation. The generated data structures outperform competitive handoptimized implementations, and the generator still allows for customization towards specific workloads
The Homeostasis Protocol: Avoiding Transaction Coordination Through Program Analysis
Datastores today rely on distribution and replication to achieve improved
performance and fault-tolerance. But correctness of many applications depends
on strong consistency properties - something that can impose substantial
overheads, since it requires coordinating the behavior of multiple nodes. This
paper describes a new approach to achieving strong consistency in distributed
systems while minimizing communication between nodes. The key insight is to
allow the state of the system to be inconsistent during execution, as long as
this inconsistency is bounded and does not affect transaction correctness. In
contrast to previous work, our approach uses program analysis to extract
semantic information about permissible levels of inconsistency and is fully
automated. We then employ a novel homeostasis protocol to allow sites to
operate independently, without communicating, as long as any inconsistency is
governed by appropriate treaties between the nodes. We discuss mechanisms for
optimizing treaties based on workload characteristics to minimize
communication, as well as a prototype implementation and experiments that
demonstrate the benefits of our approach on common transactional benchmarks
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Tracking the Flow of Ideas through the Programming Languages Literature
How have conferences like ICFP, OOPSLA, PLDI, and POPL evolved over the last 20 years? Did generalizing the Call for Papers for OOPSLA in 2007 or changing the name of the umbrella conference to SPLASH in 2010 have any effect on the kinds of papers published there? How do POPL and PLDI papers compare, topic-wise? Is there related work that I am missing? Have the ideas in O\u27Hearn\u27s classic paper on separation logic shifted the kinds of papers that appear in POPL? Does a proposed program committee cover the range of submissions expected for the conference? If we had better tools for analyzing the programming language literature, we might be able to answer these questions and others like them in a data-driven way. In this paper, we explore how topic modeling, a branch of machine learning, might help the programming language community better understand our literature