arXiv subjects

Anthony Skjellum

Publications and source records attributed to Anthony Skjellum.

22 records · Page 2Linked to original sources

arXiv2019

Extending the Message Passing Interface (MPI) with User-Level Schedules

Composability is one of seven reasons for the long-standing and continuing success of MPI. Extending MPI by composing its operations with user-level operations provides useful integration with the progress engine and completion notification methods of MPI. However, the existing extensibility mechanism in MPI (generalized requests) is not widely utilized and has significant drawbacks. MPI can be generalized via scheduled communication primitives, for example, by utilizing implementation techniques from existing MPI-3 nonblocking collectives and from forthcoming MPI-4 persistent and partitioned APIs. Non-trivial schedules are used internally in some MPI libraries; but, they are not accessible to end-users. Message-based communication patterns can be built as libraries on top of MPI. Such libraries can have comparable implementation maturity and potentially higher performance than MPI library code, but do not require intimate knowledge of the MPI implementation. Libraries can provide performance-portable interfaces that cross MPI implementation boundaries. The ability to compose additional user-defined operations using the same progress engine benefits all kinds of general purpose HPC libraries. We propose a definition for MPI schedules: a user-level programming model suitable for creating persistent collective communication composed with new application-specific sequences of user-defined operations managed by MPI and fully integrated with MPI progress and completion notification. The API proposed offers a path to standardization for extensible communication schedules involving user-defined operations. Our approach has the potential to introduce event-driven programming into MPI (beyond the tools interface), although connecting schedules with events comprises future work. Early performance results described here are promising and indicate strong overlap potential.

cs.DC↗

arXiv2017

MPIgnite: An MPI-Like Language and Prototype Implementation for Apache Spark

Scale-out parallel processing based on MPI is a 25-year-old standard with at least another decade of preceding history of enabling technologies in the High Performance Computing community. Newer frameworks such as MapReduce, Hadoop, and Spark represent industrial scalable computing solutions that have received broad adoption because of their comparative simplicity of use, applicability to relevant problems, and ability to harness scalable, distributed resources. While MPI provides performance and portability, it lacks in productivity and fault tolerance. Likewise, Spark is a specific example of a current-generation MapReduce and data-parallel computing infrastructure that addresses those goals but in turn lacks peer communication support to allow featherweight, highly scalable peer-to-peer data-parallel code sections. The key contribution of this paper is to demonstrate how to introduce the collective and point-to-point peer communication concepts of MPI into a Spark environment. This is done in order to produce performance-portable, peer-oriented and group-oriented communication services while retaining the essential, desirable properties of Spark. Additional concepts of fault tolerance and productivity are considered. This approach is offered in contrast to adding MapReduce framework as upper-middleware based on a traditional MPI implementation as baseline infrastructure.

cs.DC↗

arXiv2017

Provenance Threat Modeling

Provenance systems are used to capture history metadata, applications include ownership attribution and determining the quality of a particular data set. Provenance systems are also used for debugging, process improvement, understanding data proof of ownership, certification of validity, etc. The provenance of data includes information about the processes and source data that leads to the current representation. In this paper we study the security risks provenance systems might be exposed to and recommend security solutions to better protect the provenance information.

cs.CR↗

arXiv2016

dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures

A new scalable parallel math library, dMath, is presented in this paper that demonstrates leading scaling when using intranode, or internode, hybrid-parallelism for deep-learning. dMath provides easy-to-use distributed base primitives and a variety of domain-specific algorithms. These include matrix multiplication, convolutions, and others allowing for rapid development of highly scalable applications, including Deep Neural Networks (DNN), whereas previously one was restricted to libraries that provided effective primitives for only a single GPU, like Nvidia cublas and cudnn or DNN primitives from Nervana neon framework. Development of HPC software is difficult, labor-intensive work, requiring a unique skill set. dMath allows a wide range of developers to utilize parallel and distributed hardware easily. One contribution of this approach is that data is stored persistently on the GPU hardware, avoiding costly transfers between host and device. Advanced memory management techniques are utilized, including caching of transferred data and memory reuse through pooling. A key contribution of dMath is that it delivers performance, portability, and productivity to its specific domain of support. It enables algorithm and application programmers to quickly solve problems without managing the significant complexity associated with multi-level parallelism.

cs.NE↗