Representative workloads and principled methodologies are the foundation of performance analysis, which in turn provides the empirical grounding for much of the innovation in systems research. However, benchmarks are hard to maintain, methodologies are hard to develop, and our field moves fast. The tension between our fast-moving fields and their need to maintain their methodological foundations is a serious challenge. This paper explores that challenge through the lens of Java performance analysis. Lessons we draw extend to other languages and other fields of computer science.

To process real-world datasets, modern data-parallel systems often require extremely large amounts of memory, which are both costly and energy inefficient. Emerging non-volatile memory (NVM) technologies offer high capacity compared to DRAM and low energy compared to SSDs. Hence, NVMs have the potential to fundamentally change the dichotomy between DRAM and durable storage in Big Data processing. However, most Big Data applications are written in managed languages and executed on top of a managed runtime that already performs various dimensions of memory management. Supporting hybrid physical memories adds a new dimension, creating unique challenges in data replacement. This article proposes Panthera, a semantics-aware, fully automated memory management technique for Big Data processing over hybrid memories. Panthera analyzes user programs on a Big Data system to infer their coarse-grained access patterns, which are then passed to the Panthera runtime for efficient data placement and migration. For Big Data applications, the coarse-grained data division information is accurate enough to guide the GC for data layout, which hardly incurs overhead in data monitoring and moving. We implemented Panthera in OpenJDK and Apache Spark. Based on Big Data applications’ memory access pattern, we also implemented a new profiling-guided optimization strategy, which is transparent to applications. With this optimization, our extensive evaluation demonstrates that Panthera reduces energy by 32–53% at less than 1% time overhead on average. To show Panthera’s applicability, we extend it to QuickCached, a pure Java implementation of Memcached. Our evaluation results show that Panthera reduces energy by 28.7% at 5.2% time overhead on average.

Modern data-parallel systems such as Spark rely increasingly on in-memory computing that can significantly improve the efficiency of iterative algorithms. To process real-world datasets, modern data-parallel systems often require extremely large amounts of memory, which are both costly and energy-inefficient. Emerging non-volatile memory (NVM) technologies offers high capacity compared to DRAM and low energy compared to SSDs. Hence, NVMs have the potential to fundamentally change the dichotomy between DRAM and durable storage in Big Data processing. However, most Big Data applications are written in managed languages (e.g., Scala and Java) and executed on top of a managed runtime (e.g., the Java Virtual Machine) that already performs various dimensions of memory management. Supporting hybrid physical memories adds in a new dimension, creating unique challenges in data replacement and migration.

A powerful feature of the JavaTM programming language is its user-definable class loading policy, which when combined with the namespace independence between class loaders, allows portable implementation of semi-dynamic program transformations. Such transformations can be used for a range of purposes, including optimization and semantic extension. In this paper we present a framework for semantic extensions in Java. This framework consists of a number of simple but powerful transformations that, among other things, allow us to semantically extend Java to provide orthogonal persistence. The use of semi-dynamic program transformations lends our orthogonally persistent Java a number of important qualities, including simplicity, portability and a clean model of persistence. Significantly, our implementations are efficient and can outperform (in some cases) PJamaTM, a well-known orthogonally persistent Java, which is based on a modified virtual machine. In addition to describing the application of these transformations to orthogonally persistent Java, we foreshadow their use in a number of other contexts, including dynamic instance versioning and instrumentation. Copyright © 2000 John Wiley & Sons, Ltd.

If the iterations of a loop nest cannot be partitioned into independent tasks, data communication for data dependence is inevitable in order to execute them on parallel machines. This kind of loop nest is referred to as a DOACROSS loop nest.