Using remote memory for the Java heap enables big data analytics frameworks to process large datasets. However, the Java Virtual Machine (JVM) runtime struggles to maintain …
Parallel programs are prone to data races, which are concurrency bugs that are difficult to track and reproduce. Various attempts have been made to create or incorporate tools that …
Big data analytics frameworks, such as Spark and Giraph, need to process and cache massive datasets that do not always fit on the managed heap. Therefore, frameworks temporarily …
Scaling up the performance of managed applications on Non-Uniform Memory Access (NUMA) architectures has been a challenging task, as it requires a good understanding of the …
Big data analytics frameworks, such as Spark and Giraph, need to process and cache massive amounts of data that do not always fit on the managed heap. Therefore, frameworks …
Java benchmarking suites like Dacapo and Renaissance are employed by the research community to evaluate the performance of novel features in managed runtime systems. These suites …
The increase in computational capability of low-power Arm architectures has seen them diversify from their more traditional domain of portable battery powered devices into data …
Cache-coherent non-uniform memory access (ccNUMA) systems enable parallel applications to scale-up to thousands of cores and many terabytes of main memory. However, since remote …
Managed analytics frameworks (e.g., Spark) cache intermediate results in memory (on-heap) or storage devices (off-heap) to avoid costly recomputations, especially in graph …
In recent years, heterogeneous computing has emerged as the vital way to increase computers’ performance and energy efficiency by combining diverse hardware devices, such as …