Creating efficient, scalable dynamic parallel runtime systems for chip multiprocessors (CMPs) requires understanding the overheads that manifest at high core counts and small task sizes. In this article, we assess these overheads on Intel's Threading Building Blocks (TBB) and OpenMP. First, we use real hardware and simulations to detail various scheduler and synchronization overheads. We find that these can amount to 47% of TBB benchmark runtime and 80% of OpenMP benchmark runtime. Second, we propose load balancing techniques such as occupancy-based and criticality-guided task stealing, to boost performance. Overall, our study provides valuable insights for creating robust, scalable runtime libraries.

The design process for chip multiprocessors (CMPs) requires extremely long simulation times to explore performance, power, and thermal issues, particularly when operating system (OS) effects are included. In response, our novel FPGA-based emulation methodology models a full CMP design including applications and an OS. Activity counters programmed into the cores feed per-component microarchitectural power models. These models achieve under 10 % error compared to detailed gate-level simulations. Our method retains software flexibility, but offers up to 35 × speedup compared to full-system software simulations. We present our approach by emulating a 2-core Leon3 cache-coherent multiprocessor running Linux and parallel benchmarks. In an example case study, our emulated system uses activity counts (a proxy for temperature) to guide process migration between the CMP cores. Overall, this paper’s methodology makes possible detailed power and thermal studies of CMPs and their operating systems.

Managing power concerns in microprocessors has become a pressing research problem across the domains of computer architecture, CAD, and compilers. As a result, several parameterized cycle-level power simulators have been introduced. While these simulators can be quite useful for microarchitectural studies, their generality limits how accurate they can be for any one chip family. Furthermore, their hardware focus means that they do not explicitly enable studying the interaction of different software layers, such as Java applications and their underlying runtime system software. This paper describes and evaluates XTREM, a power-simulation tool tailored for the Intel XScale microarchitecture. In building XTREM, our goals were to develop a microarchitecture simulator that, while still offering size parameterizations for cache and other structures, more accurately reflected a realistic processor pipeline. We present a detailed set of validations based on multimeter power measurements and hardware performance counter sampling. XTREM exhibits an average performance error of only 6.5% and an even smaller average power error: 4%. The paper goes on to present an application study enabled by the simulator. Namely, we use XTREM to produce an energy consumption breakdown for Java CDC and CLDC applications. Our simulator measurements indicate that a large percentage of the total energy consumption (up to 35%) is devoted to the virtual machine's support functions.

Computer architecture has experienced a major paradigm shift from focusing only on raw performance to considering power-performance efficiency as the defining factor of the emerging systems. Along with this shift has come increased interest in workload characterization. This interest fuels two closely related areas of research. First, various studies explore the properties of workload variations and develop methods to identify and track different execution behavior, commonly referred to as "phase analysis". Second, a large complementary set of research studies dynamic, on-the-fly system management techniques that can adaptively respond to these differences in application behavior. Both of these lines of work have produced very interesting and widely useful results. Thus far, however, there exists only a weak link between these conceptually related areas, especially for real-system studies. Our work aims to strengthen this link by demonstrating a real-system implementation of a runtime phase predictor that works cooperatively with on-the-fly dynamic management. We describe a fully-functional deployed system that performs accurate phase predictions on running applications. The key insight of our approach is to draw from prior branch predictor designs to create a phase history table that guides predictions. To demonstrate the value of our approach, we implement a prototype system that uses it to guide dynamic voltage and frequency scaling. Our runtime phase prediction methodology achieves above 90% prediction accuracies for many of the experimented benchmarks. For highly variable applications, our approach can reduce mispredictions by more than 6X over commonly-used statistical approaches. Dynamic frequency and voltage scaling, when guided by our runtime phase predictor, achieves energy-delay product improvements as high as 34% for benchmarks with non-negligible variability, on average 7% better than previous methods and 18% better than a baseline unmanaged system

This paper demonstrates a first-order, linear power estimation model ha uses performance counters to estimate run-time CPU and memory power consumption of the Intel PXA255 processor. Our model uses a set of power weights that map hardware performance counter values to processor and memory power consumption. Power weights are derived offline once per processor voltage and frequency configuration using parameter estimation echniques. They can be applied in a dynamic voltage/frequency scaling environment by setting six descriptive parameters. We have tested our model using a wide selection of benchmarks including SPEC2000, Java CDC and Java CLDC programming environments. The accuracy is quite good; average estimated power consumption is within 4% of he measured average CPU power consumption. We believe such power estimation schemes can serve as a foundation for intelligent, power-aware embedded systems tha dynamically adapt to the device's power consumption

Managing power concerns in icroprocessors has become a pressing research problem across the domains of computer architecture, CAD, and compilers. As a result, several parameterized cycle-level power simulators have been introduced. While these simulators can be quite useful for microarchitectural studies, their generality limits how accurate they can be for any one chip family. Furthermore, their hardware focus means that they do not explicitly enable studying the interaction of different software layers, such as Java applications and their underlying Runtime system software.This paper describes and evaluates XTREM, a power simulation tool tailored for the Intel XScale icroarchitecture. In building XTREM, our goals were to develop a icroarchitecture simulator that, while still offering size parameterizations for cache, TLB, etc., more accurately reflected a realistic processor pipeline. We present a detailed set of validations based on ultimeter power measurements and hardware performance counter sampling. Based on these validations across a wide range of stressmarks, Java benchmarks, and non-Java benchmarks, XTREM has an average performance error of only 6.5% and an even smaller average power error: 4%. The paper goes on to present a selection of application studies enabled by the simulator. For example, presenting power behavior vs. time for selected embedded C and Java CLDC benchmarks, we can make power distinctions between the two programming domains as well as distinguishing Java application (JITted code) power from Java Runtime system power. We also study how the Intel XScale core 's power consumption varies for different data activity factors, creating power swings as large as 50mW for a 200Mhz core. We are planning to release XTREM for wider use, and feel that it offers a useful step forward for compiler and embedded software designers.