¿Son las GPUs dispositivos eficientes energéticamente?

With energy consumption emerging as one of the biggest issues in the development of HPC (High Performance Computing) applications, the importance of detailed power-related research works becomes a priority. In the last years, GPU coprocessors have been increasingly used to accelerate many of these high-priced systems even though they are embedding millions of transistors on their chips delivering an immediate increase on power consumption necessities. This paper analyzes a set of applications from the Rodinia benchmark suite in terms of CPU and GPU performance and energy consumption. Specifically, it compares single-threaded and multi-threaded CPU versions with GPU implementations, and characterize the execution time, true instant power and average energy consumption to test the idea that GPUs are power-hungry computing devices.Con el consumo de energía emergiendo como uno de los mayores problemas en el desarrollo de aplicaciones HPC (High Performance Computing), la importancia de trabajos específicos de investigación en este campo se convierte en una prioridad. En los últimos años, los coprocesadores GPU se han utilizado frecuentemente para acelerar muchos de estos costosos sistemas, a pesar de que incorporan millones de transistores en sus chips, lo que genera un aumento considerable en los requerimientos de energía. Este artículo analiza un conjunto de aplicaciones del benchmark Rodinia en términos de rendimiento y consumo de energía de CPU y GPU. Específicamente, se comparan las versiones secuenciales y multihilo en CPU con implementaciones GPU, caracterizando el tiempo de ejecución, la potencia real instantánea y el consumo promedio de energía, con el objetivo de probar la idea de que las GPU son dispositivos de baja eficiencia energética.Facultad de Informátic

¿Son las GPUs dispositivos eficientes energéticamente?

With energy consumption emerging as one of the biggest issues in the development of HPC (High Performance Computing) applications, the importance of detailed power-related research works becomes a priority. In the last years, GPU coprocessors have been increasingly used to accelerate many of these high-priced systems even though they are embedding millions of transistors on their chips delivering an immediate increase on power consumption necessities. This paper analyzes a set of applications from the Rodinia benchmark suite in terms of CPU and GPU performance and energy consumption. Specifically, it compares single-threaded and multi-threaded CPU versions with GPU implementations, and characterize the execution time, true instant power and average energy consumption to test the idea that GPUs are power-hungry computing devices.Con el consumo de energía emergiendo como uno de los mayores problemas en el desarrollo de aplicaciones HPC (High Performance Computing), la importancia de trabajos específicos de investigación en este campo se convierte en una prioridad. En los últimos años, los coprocesadores GPU se han utilizado frecuentemente para acelerar muchos de estos costosos sistemas, a pesar de que incorporan millones de transistores en sus chips, lo que genera un aumento considerable en los requerimientos de energía. Este artículo analiza un conjunto de aplicaciones del benchmark Rodinia en términos de rendimiento y consumo de energía de CPU y GPU. Específicamente, se comparan las versiones secuenciales y multihilo en CPU con implementaciones GPU, caracterizando el tiempo de ejecución, la potencia real instantánea y el consumo promedio de energía, con el objetivo de probar la idea de que las GPU son dispositivos de baja eficiencia energética.Facultad de Informátic

Run-Time Power Modelling in Embedded GPUs with Dynamic Voltage and Frequency Scaling

This paper investigates the application of a robust CPU-based power modelling methodology that performs an automatic search of explanatory events derived from performance counters to embedded GPUs. A 64-bit Tegra TX1 SoC is configured with DVFS enabled and multiple CUDA benchmarks are used to train and test models optimized for each frequency and voltage point. These optimized models are then compared with a simpler unified model that uses a single set of model coefficients for all frequency and voltage points of interest. To obtain this unified model, a number of experiments are conducted to extract information on idle, clock and static power to derive power usage from a single reference equation. The results show that the unified model offers competitive accuracy with an average 5\% error with four explanatory variables on the test data set and it is capable to correctly predict the impact of voltage, frequency and temperature on power consumption. This model could be used to replace direct power measurements when these are not available due to hardware limitations or worst-case analysis in emulation platforms

CampProf: A Visual Performance Analysis Tool for Memory Bound GPU Kernels

Current GPU tools and performance models provide some common architectural insights that guide the programmers to write optimal code. We challenge these performance models, by modeling and analyzing a lesser known, but very severe performance pitfall, called 'Partition Camping', in NVIDIA GPUs. Partition Camping is caused by memory accesses that are skewed towards a subset of the available memory partitions, which may degrade the performance of memory-bound CUDA kernels by up to seven-times. No existing tool can detect the partition camping effect in CUDA kernels. We complement the existing tools by developing 'CampProf', a spreadsheet based, visual analysis tool, that detects the degree to which any memory-bound kernel suffers from partition camping. In addition, CampProf also predicts the kernel's performance at all execution configurations, if its performance parameters are known at any one of them. To demonstrate the utility of CampProf, we analyze three different applications using our tool, and demonstrate how it can be used to discover partition camping. We also demonstrate how CampProf can be used to monitor the performance improvements in the kernels, as the partition camping effect is being removed. The performance model that drives CampProf was developed by applying multiple linear regression techniques over a set of specific micro-benchmarks that simulated the partition camping behavior. Our results show that the geometric mean of errors in our prediction model is within 12% of the actual execution times. In summary, CampProf is a new, accurate, and easy-to-use tool that can be used in conjunction with the existing tools to analyze and improve the overall performance of memory-bound CUDA kernels

GPU Performance and Power Consumption Analysis: A DCT based denoising application

It is known that energy and power consumption are becoming serious metrics in the design of high performance workstations because of heat dissipation problems. In the last years, GPU accelerators have been integrating many of these expensive systems despite they are embedding more and more transistors on their chips producing a quick increase of power consumption requirements. This paper analyzes an image processing application, in particular a Discrete Cosine Transform denoising algorithm, in terms of CPU and GPU performance and energy consumption. Specifically, we want to compare single-threaded and multithreaded CPU versions with a GPU version, and characterize the execution time, true instant power and average energy consumption to deflate the idea that GPUs are non-green computing devices.XVIII Workshop de Procesamiento Distribuido y Paralelo (WPDP).Red de Universidades con Carreras en Informática (RedUNCI

Power Modeling for Heterogeneous Processors

As power becomes an ever more important design consideration, there is a need for accurate power models at all stages of the design process. While power models are available for CPUs and GPUs, only simple models are available for heterogeneous processors. We present a micro-benchmarkbased modeling technique that can be used for chip multiprocessor (CMPs) and accelerated processing units (APUs). We use our approach to model power on an Intel Xeon CPU and an AMD Fusion heterogeneous processor. The resulting error rate for the Xeon’s model is below 3 % and is only 7% for the Fusion. We also present a method to reduce the number of benchmarks required to create these models. Instead of running micro-benchmarks for every combination of factors (e.g. different operations or memory access patterns), we cluster similar micro-benchmarks to avoid unnecessary simulations. We show that it is possible to eliminate as many as 93 % of the compute micro-benchmarks, while still producing power models having less than 10 % error rate