Lattice agreement is a key decision problem in distributed systems. In this problem, processes start with input values from a lattice, and must learn (non-trivial) values that form a chain. Unlike consensus, which is impossible in the presence of even a single process failure, lattice agreement has been shown to be decidable in the presence of failures. In this paper, we consider lattice agreement problems in asynchronous, message passing systems. We present an algorithm for the lattice agreement problem that guarantees liveness as long as a majority of the processes are non-faulty. The algorithm has a time complexity of O(N ) message delays, where N is the number of processes. We then introduce the generalized lattice agreement problem, where each process receives a (potentially unbounded) sequence of values from an infinite lattice and must learn a sequence of increasing values such that the union of all learnt sequences is a chain and every proposed value is eventually learnt. We present a wait-free algorithm for solving generalized lattice agreement. The algorithm guarantees that every value received by a correct process is learnt in O(N ) message delays. We show that this algorithm can be used to implement a class of replicated state machines where (a) commands can be classified as reads and updates, and (b) all update commands commute. This algorithm can be used to realize serializable and linearizable replicated versions of commonly used data types.

Abstract. Context-sensitive points-to analysis is critical for several program optimizations. However, as the number of contexts grows exponentially, storage requirements for the analysis increase tremendously for large programs, making the analysis non-scalable. We propose a scalable flow-insensitive context-sensitive inclusion-based points-to analysis that uses a specially designed multi-dimensional bloom filter to store the points-to information. Two key observations motivate our proposal: (i) points-to information (between pointer-object and between pointerpointer) is sparse, and (ii) moving from an exact to an approximate representation of points-to information only leads to reduced precision without affecting correctness of the (may-points-to) analysis. By using an approximate representation a multi-dimensional bloom filter can significantly reduce the memory requirements with a probabilistic bound on loss in precision. Experimental evaluation on SPEC 2000 benchmarks and two large open source programs reveals that with an average storage requirement of 4MB, our approach achieves almost the same precision (98.6%) as the exact implementation. By increasing the average memory to 27MB, it achieves precision upto 99.7% for these benchmarks. Using Mod/Ref analysis as the client, we find that the client analysis is not affected that often even when there is some loss of precision in the points-to representation. We find that the NoModRef percentage is within 2% of the exact analysis while requiring 4MB (maximum 15MB) memory and less than 4 minutes on average for the points-to analysis. Another major advantage of our technique is that it allows to trade off precision for memory usage of the analysis.

As network traffic continues to increase and with the requirement to process packets at line rates, high performance routers need to forward millions of packets every second. Even with an efficient lookup algorithm like the LC-trie, each packet needs upto 5 memory accesses. Earlier work shows that a single cache for the nodes of an LC-trie can reduce the number of external memory accesses.We observe that the locality characteristics of the level-one nodes of an LC-trie are significantly different from those of lower-level nodes. Hence, we propose a heterogeneously segmented cache architecture (HSCA) which uses separate caches for level-one and lower-level nodes each with carefully chosen sizes. We further improve the hit rate of the level-one nodes cache by introducing a weight-based replacement policy and an intelligent index bit selection scheme. To evaluate our cache scheme with realistic traces, we propose a synthetic trace generation method which emulates real traces and can generate traces with varying locality characteristics. The base HSCA scheme gives us upto 16% reduction in misses over the unified scheme. The optimizations further enhance this improvement to upto 25% for core router traces.

This article traces systems of exchange concerning the life sciences and capital and how they configure subjectivity in the United States and India. This is done through case studies concerning the emergence of personalized medicine in the two locales. In the U.S. case, I argue for the configuration of the subjects of personalized medicine as sovereign consumers; in the Indian case, I argue for their configuration as experimental subjects. I situate these arguments in the context of epistemologies of genomics and the consolidation of systems of speculative capitalism.

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