Compiler and system for resilient distributed heterogeneous graph analytics

Graph analytics systems are used in a wide variety of applications including health care, electronic circuit design, machine learning, and cybersecurity. Graph analytics systems must handle very large graphs such as the Facebook friends graph, which has more than a billion nodes and 200 billion edges. Since machines have limited main memory, distributed-memory clusters with sufficient memory and computation power are required for processing of these graphs. In distributed graph analytics, the graph is partitioned among the machines in a cluster, and communication between partitions is implemented using a substrate like MPI. However, programming distributed-memory systems are not easy and the recent trend towards the processor heterogeneity has added to this complexity. To simplify the programming of graph applications on such platforms, this dissertation first presents a compiler called Abelian that translates shared-memory descriptions of graph algorithms written in the Galois programming model into efficient code for distributed-memory platforms with heterogeneous processors. An important runtime parameter to the compiler-generated distributed code is the partitioning policy. We present an experimental study of partitioning strategies for distributed work-efficient graph analytics applications on different CPU architecture clusters at large scale (up to 256 machines). Based on the study we present a simple rule of thumb to select among myriad policies. Another challenge of distributed graph analytics that we address in this dissertation is to deal with machine fail-stop failures, which is an important concern especially for long-running graph analytics applications on large clusters. We present a novel communication and synchronization substrate called Phoenix that leverages the algorithmic properties of graph analytics applications to recover from faults with zero overheads during fault-free execution and show that Phoenix is 24x faster than previous state-of-the-art systems. In this dissertation, we also look at the new opportunities for graph analytics on massive datasets brought by a new kind of byte-addressable memory technology with higher density and lower cost than DRAM such as intel Optane DC Persistent Memory. This enables the design of affordable systems that support up to 6TB of randomly accessible memory. In this dissertation, we present key runtime and algorithmic principles to consider when performing graph analytics on massive datasets on Optane DC Persistent Memory as well as highlight ideas that apply to graph analytics on all large-memory platforms. Finally, we show that our distributed graph analytics infrastructure can be used for a new domain of applications, in particular, embedding algorithms such as Word2Vec. Word2Vec trains the vector representations of words (also known as word embeddings) on large text corpus and resulting vector embeddings have been shown to capture semantic and syntactic relationships among words. Other examples include Node2Vec, Code2Vec, Sequence2Vec, etc (collectively known as Any2Vec) with a wide variety of uses. We formulate the training of such applications as a graph problem and present GraphAny2Vec, a distributed Any2Vec training framework that leverages the state-of-the-art distributed heterogeneous graph analytics infrastructure developed in this dissertation to scale Any2Vec training to large distributed clusters. GraphAny2Vec also demonstrates a novel way of combining model gradients during training, which allows it to scale without losing accuracyComputer Science

Pregelix: Big(ger) Graph Analytics on A Dataflow Engine

There is a growing need for distributed graph processing systems that are capable of gracefully scaling to very large graph datasets. Unfortunately, this challenge has not been easily met due to the intense memory pressure imposed by process-centric, message passing designs that many graph processing systems follow. Pregelix is a new open source distributed graph processing system that is based on an iterative dataflow design that is better tuned to handle both in-memory and out-of-core workloads. As such, Pregelix offers improved performance characteristics and scaling properties over current open source systems (e.g., we have seen up to 15x speedup compared to Apache Giraph and up to 35x speedup compared to distributed GraphLab), and makes more effective use of available machine resources to support Big(ger) Graph Analytics

PiCo: A Domain-Specific Language for Data Analytics Pipelines

In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models—for which only informal (and often confusing) semantics is generally provided—all share a common under- lying model, namely, the Dataflow model. Using this model as a starting point, it is possible to categorize and analyze almost all aspects about Big Data analytics tools from a high level perspective. This analysis can be considered as a first step toward a formal model to be exploited in the design of a (new) framework for Big Data analytics. By putting clear separations between all levels of abstraction (i.e., from the runtime to the user API), it is easier for a programmer or software designer to avoid mixing low level with high level aspects, as we are often used to see in state-of-the-art Big Data analytics frameworks. From the user-level perspective, we think that a clearer and simple semantics is preferable, together with a strong separation of concerns. For this reason, we use the Dataflow model as a starting point to build a programming environment with a simplified programming model implemented as a Domain-Specific Language, that is on top of a stack of layers that build a prototypical framework for Big Data analytics. The contribution of this thesis is twofold: first, we show that the proposed model is (at least) as general as existing batch and streaming frameworks (e.g., Spark, Flink, Storm, Google Dataflow), thus making it easier to understand high-level data-processing applications written in such frameworks. As result of this analysis, we provide a layered model that can represent tools and applications following the Dataflow paradigm and we show how the analyzed tools fit in each level. Second, we propose a programming environment based on such layered model in the form of a Domain-Specific Language (DSL) for processing data collections, called PiCo (Pipeline Composition). The main entity of this programming model is the Pipeline, basically a DAG-composition of processing elements. This model is intended to give the user an unique interface for both stream and batch processing, hiding completely data management and focusing only on operations, which are represented by Pipeline stages. Our DSL will be built on top of the FastFlow library, exploiting both shared and distributed parallelism, and implemented in C++11/14 with the aim of porting C++ into the Big Data world

Exploring Application Performance on Emerging Hybrid-Memory Supercomputers

Next-generation supercomputers will feature more hierarchical and heterogeneous memory systems with different memory technologies working side-by-side. A critical question is whether at large scale existing HPC applications and emerging data-analytics workloads will have performance improvement or degradation on these systems. We propose a systematic and fair methodology to identify the trend of application performance on emerging hybrid-memory systems. We model the memory system of next-generation supercomputers as a combination of "fast" and "slow" memories. We then analyze performance and dynamic execution characteristics of a variety of workloads, from traditional scientific applications to emerging data analytics to compare traditional and hybrid-memory systems. Our results show that data analytics applications can clearly benefit from the new system design, especially at large scale. Moreover, hybrid-memory systems do not penalize traditional scientific applications, which may also show performance improvement.Comment: 18th International Conference on High Performance Computing and Communications, IEEE, 201