Compiler and runtime support for shared memory parallelization of data mining algorithms

Abstract. Data mining techniques focus on finding novel and useful patterns or models from large datasets. Because of the volume of the data to be analyzed, the amount of computation involved, and the need for rapid or even interactive analysis, data mining applications require the use of parallel machines. We have been developing compiler and runtime support for developing scalable implementations of data mining algorithms. Our work encompasses shared memory parallelization, distributed memory parallelization, and optimizations for processing disk-resident datasets. In this paper, we focus on compiler and runtime support for shared memory parallelization of data mining algorithms. We have developed a set of parallelization techniques that apply across algorithms for a variety of mining tasks. We describe the interface of the middleware where these techniques are implemented. Then, we present compiler techniques for translating data parallel code to the middleware specification. Finally, we present a brief evaluation of our compiler using apriori association mining and k-means clustering.

An integrated runtime and compile-time approach for parallelizing structured and block structured applications

Scientific and engineering applications often involve structured meshes. These meshes may be nested (for multigrid codes) and/or irregularly coupled (called multiblock or irregularly coupled regular mesh problems). A combined runtime and compile-time approach for parallelizing these applications on distributed memory parallel machines in an efficient and machine-independent fashion was described. A runtime library which can be used to port these applications on distributed memory machines was designed and implemented. The library is currently implemented on several different systems. To further ease the task of application programmers, methods were developed for integrating this runtime library with compilers for HPK-like parallel programming languages. How this runtime library was integrated with the Fortran 90D compiler being developed at Syracuse University is discussed. Experimental results to demonstrate the efficacy of our approach are presented. A multiblock Navier-Stokes solver template and a multigrid code were experimented with. Our experimental results show that our primitives have low runtime communication overheads. Further, the compiler parallelized codes perform within 20 percent of the code parallelized by manually inserting calls to the runtime library

Interprocedural Compilation of Irregular Applications for Distributed Memory Machines

Data parallel languages like High Performance Fortran (HPF) are emerging as the architecture independent mode of programming distributed memory parallel machines. In this paper, we present the interprocedural optimizations required for compiling applications having irregular data access patterns, when coded in such data parallel languages. We have developed an Interprocedural Partial Redundancy Elimination (IPRE) algorithm for optimized placement of runtime preprocessing routine and collective communication routines inserted for managing communication in such codes. We also present three new interprocedural optimizations: placement of scatter routines, deletion of data structures and use of coalescing and incremental routines. We then describe how program slicing can be used for further applying IPRE in more complex scenarios. We have done a preliminary implementation of the schemes presented here using the Fortran D compilation system as the necessary infrastructure. We present experimental results from two codes compiled using our system to demonstrate the efficacy of the presented schemes. (Also cross-referenced as UMIACS-TR-95-43

ForensiBlock: A Provenance-Driven Blockchain Framework for Data Forensics and Auditability

Maintaining accurate provenance records is paramount in digital forensics, as they underpin evidence credibility and integrity, addressing essential aspects like accountability and reproducibility. Blockchains have several properties that can address these requirements. Previous systems utilized public blockchains, i.e., treated blockchain as a black box, and benefiting from the immutability property. However, the blockchain was accessible to everyone, giving rise to security concerns and moreover, efficient extraction of provenance faces challenges due to the enormous scale and complexity of digital data. This necessitates a tailored blockchain design for digital forensics. Our solution, Forensiblock has a novel design that automates investigation steps, ensures secure data access, traces data origins, preserves records, and expedites provenance extraction. Forensiblock incorporates Role-Based Access Control with Staged Authorization (RBAC-SA) and a distributed Merkle root for case tracking. These features support authorized resource access with an efficient retrieval of provenance records. Particularly, comparing two methods for extracting provenance records off chain storage retrieval with Merkle root verification and a brute-force search the offchain method is significantly better, especially as the blockchain size and number of cases increase. We also found that our distributed Merkle root creation slightly increases smart contract processing time but significantly improves history access. Overall, we show that Forensiblock offers secure, efficient, and reliable handling of digital forensic dataComment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Interprocedural Partial Redundancy Elimination and its Application to Distributed Memory Compilation

Partial Redundancy Elimination (PRE) is a general scheme for suppressing partial redundancies which encompasses traditional optimizations like loop invariant code motion and redundant code elimination. In this paper we address the problem of performing this optimization interprocedurally. We use interprocedural partial redundancy elimination for placement of communication and communication preprocessing statements while compiling for distributed memory parallel machines. (Also cross-referenced as UMIACS-TR-95-42

An Interprocedural Framework for Placement of Asychronous I/O Operations

Overlapping memory accesses with computations is a standard technique for improving performance on modern architectures, which have deep memory hierarchies. In this paper, we present a compiler technique for overlapping accesses to secondary memory (disks) with computation. We have developed an Interprocedural Balanced Code Placement (IBCP) framework, which performs analysis on arbitrary recursive procedures and arbitrary control flow and replaces synchronous I/O operations with a balanced pair of asynchronous operations. We demonstrate how this analysis is useful for applications which perform frequent and large accesses to secondary memory, including applications which snapshot or checkpoint their computations or out-of-core applications. (Also cross-referenced as UMIACS-TR-95-114

Data Parallel Programming in an Adaptive Environment

For better utilization of computing resources, it is important to consider parallel programming environments in which the number of available processors varies at runtime. In this paper, we discuss runtime support for data parallel programming in such an adaptive environment. Executing data parallel programs in an adaptive environment requires redistributing data when the number of processors changes, and also requires determining new loop bounds and communication patterns for the new set of processors. We have developed a runtime library to provide this support. We discuss how the runtime library can be used by compilers to generate code for an adaptive environment. We also present performance results for a multiblock Navier-Stokes solver run on a network of workstations using PVM for message passing. Our experiments show that if the number of processors is not varied frequently, the cost of data redistribution is not significant compared to the time required for the actual computations. (Also cross-referenced as UMIACS-TR-94-109