Approaches to Interpreter Composition

In this paper, we compose six different Python and Prolog VMs into 4 pairwise compositions: one using C interpreters; one running on the JVM; one using meta-tracing interpreters; and one using a C interpreter and a meta-tracing interpreter. We show that programs that cross the language barrier frequently execute faster in a meta-tracing composition, and that meta-tracing imposes a significantly lower overhead on composed programs relative to mono-language programs.Comment: 33 pages, 1 figure, 9 table

DReAM: An approach to estimate per-Task DRAM energy in multicore systems

Accurate per-task energy estimation in multicore systems would allow performing per-task energy-aware task scheduling and energy-aware billing in data centers, among other applications. Per-task energy estimation is challenged by the interaction between tasks in shared resources, which impacts tasks’ energy consumption in uncontrolled ways. Some accurate mechanisms have been devised recently to estimate per-task energy consumed on-chip in multicores, but there is a lack of such mechanisms for DRAM memories. This article makes the case for accurate per-task DRAM energy metering in multicores, which opens new paths to energy/performance optimizations. In particular, the contributions of this article are (i) an ideal per-task energy metering model for DRAM memories; (ii) DReAM, an accurate yet low cost implementation of the ideal model (less than 5% accuracy error when 16 tasks share memory); and (iii) a comparison with standard methods (even distribution and access-count based) proving that DReAM is much more accurate than these other methods.Peer ReviewedPostprint (author's final draft

DReAM: Per-task DRAM energy metering in multicore systems

Interaction across applications in DRAM memory impacts its energy consumption. This paper makes the case for accurate per-task DRAM energy metering in multicores, which opens new paths to energy/performance optimizations, such as per-task energy-aware task scheduling and energy-aware billing in datacenters. In particular, the contributions of this paper are (i) an ideal per-task energy metering model for DRAM memories; (ii) DReAM, an accurate, yet low cost, implementation of the ideal model (less than 5% accuracy error when 16 tasks share memory); and (iii) a comparison with standard methods (even distribution and access-count based) proving that DReAM is more accurate than these other methods.This work has been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2012-34557, the HiPEAC Network of Excellence, by the European Research Council under the European Union’s 7th FP, ERC Grant Agreement n. 321253, and by a joint study agreement between IBM and BSC (number W1361154). Qixiao Liu has also been funded by the Chinese Scholarship Council under grant 2010608015.Postprint (published version

Decrypting The Java Gene Pool: Predicting Objects' Lifetimes with Micro-patterns

Pretenuring long-lived and immortal objects into infrequently or never collected regions reduces garbage collection costs significantly. However, extant approaches either require computationally expensive, application-specific, off-line profiling, or consider only allocation sites common to all programs, i.e. invoked by the virtual machine rather than application programs. In contrast, we show how a simple program analysis, combined with an object lifetime knowledge bank, can be exploited to match both runtime system and application program structure with object lifetimes. The complexity of the analysis is linear in the size of the program, so need not be run ahead of time. We obtain performance gains between 6-77% in GC time against a generational copying collector for several SPEC jvm98 programs

MERIC and RADAR generator: tools for energy evaluation and runtime tuning of HPC applications

This paper introduces two tools for manual energy evaluation and runtime tuning developed at IT4Innovations in the READEX project. The MERIC library can be used for manual instrumentation and analysis of any application from the energy and time consumption point of view. Besides tracing, MERIC can also change environment and hardware parameters during the application runtime, which leads to energy savings. MERIC stores large amounts of data, which are difficult to read by a human. The RADAR generator analyses the MERIC output files to find the best settings of evaluated parameters for each instrumented region. It generates a Open image in new window report and a MERIC configuration file for application production runs