Azure Data Lake Store (ADLS) is a fully-managed, elastic, scalable, and secure file system that supports Hadoop distributed file system (HDFS) and Cosmos semantics. It is specifically designed and optimized for a broad spectrum of Big Data analytics that depend on a very high degree of parallel reads and writes, as well as collocation of compute and data for high bandwidth and low-latency access. It brings together key components and features of Microsoft?s Cosmos file system-long used by internal customers at Microsoft and HDFS, and is a unified file storage solution for analytics on Azure. Internal and external workloads run on this unified platform. Distinguishing aspects of ADLS include its design for handling multiple storage tiers, exabyte scale, and comprehensive security and data sharing features. We present an overview of ADLS architecture, design points, and performance.

We study the systematic development of constraint logic programs from the viewpoint of "skeletons and techniques" as described by M. Kirschenbaum et al. (1994). We describe a number of fundamental skeleton classes for CLP, and generalize the notion of skeletons to deal with non-structural recursion. Then we describe a range of useful techniques for extending these skeletons. Furthermore, we introduce important classes of techniques that alter the control flow of skeletons in certain well-defined and desirable ways.>

An abstract machine is described for the CLP(ℜ) programming language. It is intended as a first step toward enabling CLP(ℜ) programs to be executed with efficiency approaching that of conventional languages. The core Constraint Logic Arithmetic Machine (CLAM) extends the Warren Abstract Machine (WAM) for compiling Prolog with facilities for handling real arithmetic constraints. The full CLAM includes facilities for taking advantage of information obtained from global program analysis.

quantification, side-effects, and assignment.TheseWe report some initial results regarding the efficient compilation of the second-order polymorphic A-calculus (Fz).Our compiler makes strong use of type information and the strong normalization and Church-Rosser properties of F2.Among the conceptual tools we develop is a notion of observational equivalence for F2, which we use to outline a proof that our compiler preserves the observable behavior of programs.Our technique compiles functions of well-understood inductive types to non-functional data structures, and computation is no longer just /reduction.A limited form of partial evaluation with a simple "Eureka" step is used to help circumvent provable inefficiencies of some functions in the pure polymorphic Lcalculus.For example, the usual predecessor function on Church numerals is compiled to a constant-time function.

In constraint logic programming (CLP) systems, the standard technique for dealing with hard constraints is to delay solving them until additional constraints reduce them to a simpler form.For example, the CLP (7?) system delays the solving of nonlinear equations until they become linear, when certain variables become ground.In a naive implement ation, the overhead of delaying and awakening constraints could render a CLP system impractical.

A parallel resolution theorem prover, called Parthenon, that handles first-order logic is described. Parthenon is apparently the first general-purpose theorem prover to be developed for a multiprocessor. The system is based on a modification of D.H.D. Warren's SRI model (Int. Symp. on Logic Prog., pp.92-101, 1987) for OR-parallelism and implements a variant of D.W. Loveland's (J. ACM, vol.15, pp.236-51, 1968) model elimination procedure. It has been evaluated on various shared memory multiprocessors, including a 16-processor Encore Multimax. The authors have found that typical theorem-proving problems exhibit a great deal of potential parallelism. Parthenon has been able to exploit much of this parallelism, producing both impressive absolute run times and near-linear speed-up curves.>