24,330 research outputs found
Performance and power comparisons between Fermi and Cypress GPUs
In recent years, modern graphics processing units have been widely adopted in high performance computing areas to solve large scale computation problems. The leading GPU manufacturers Nvidia and AMD have introduced series of products to the market. While sharing many similar design concepts, GPUs from these two manufacturers differ in several aspects on processor cores and the memory subsystem. In this work, we conduct a comprehensive study to characterize and compare the architectural features of Nvidia’s Fermi and AMD’s Cypress GPUs. We first investigate the performance and power consumptions of an AMD Cypress GPU. By employing a rigorous statistical model to analyze the execution behaviors of representative general-purpose GPU (GPGPU) applications, we conduct insightful investigations on the target GPU architecture. Our results demonstrate that the GPU execution throughput and the power dissipation are dependent on different architectural variables. Furthermore, we design a set of micro-benchmarks to study the power consumption features of different function units on the GPU. Based on those results, we derive instructive principles that can guide the design of power-efficient high performance computing systems. We then make the concentration shift to the Nvidia Fermi GPU and compare it with the product from AMD. Our results indicate that these two products have diverse advantages that are reflected in their performance for different sets of applications. In addition, we also compare the energy efficiencies of these two platforms since power/energy consumption is a major concern in the high performance computing system
Intelligent scheduling for simultaneous CPU-GPU applications
Heterogeneous computing systems with both general purpose multicore central processing units (CPU) and specialized accelerators has emerged recently. Graphics processing unit (GPU) is the most widely used accelerator. To fully utilize such a heterogeneous system’s full computing power, coordination between the two distinct devices, CPU and GPU, is necessary. Previous research has addressed this issue of partitioning the workloads between CPU and GPU from various aspects for regular applications which have high parallelism and little data dependent control flows. However, it is still not clear how irregular applications, which behave differently on different inputs, could be efficiently scheduled on such heterogeneous computing systems. Since CPUs and GPUs have different characteristics, task chunks of these irregular applications show preference, or affinity, to a particular device in heterogeneous computing systems. In this work, we show that by using the method of allocating workloads at task chunk granularity based on each chunk’s device affinity, accompanied with work-stealing as the load balancing mechanism, we can achieve a performance improvement of as much as 1.5x over traditional ratio-based allocation, and up to 5x over naive GPU-only allocation on three irregular graph analytics applications
A Review on Software Architectures for Heterogeneous Platforms
The increasing demands for computing performance have been a reality
regardless of the requirements for smaller and more energy efficient devices.
Throughout the years, the strategy adopted by industry was to increase the
robustness of a single processor by increasing its clock frequency and mounting
more transistors so more calculations could be executed. However, it is known
that the physical limits of such processors are being reached, and one way to
fulfill such increasing computing demands has been to adopt a strategy based on
heterogeneous computing, i.e., using a heterogeneous platform containing more
than one type of processor. This way, different types of tasks can be executed
by processors that are specialized in them. Heterogeneous computing, however,
poses a number of challenges to software engineering, especially in the
architecture and deployment phases. In this paper, we conduct an empirical
study that aims at discovering the state-of-the-art in software architecture
for heterogeneous computing, with focus on deployment. We conduct a systematic
mapping study that retrieved 28 studies, which were critically assessed to
obtain an overview of the research field. We identified gaps and trends that
can be used by both researchers and practitioners as guides to further
investigate the topic
A Modeling Approach based on UML/MARTE for GPU Architecture
Nowadays, the High Performance Computing is part of the context of embedded
systems. Graphics Processing Units (GPUs) are more and more used in
acceleration of the most part of algorithms and applications. Over the past
years, not many efforts have been done to describe abstractions of applications
in relation to their target architectures. Thus, when developers need to
associate applications and GPUs, for example, they find difficulty and prefer
using API for these architectures. This paper presents a metamodel extension
for MARTE profile and a model for GPU architectures. The main goal is to
specify the task and data allocation in the memory hierarchy of these
architectures. The results show that this approach will help to generate code
for GPUs based on model transformations using Model Driven Engineering (MDE).Comment: Symposium en Architectures nouvelles de machines (SympA'14) (2011
Architecture-Aware Optimization on a 1600-core Graphics Processor
The graphics processing unit (GPU) continues to
make significant strides as an accelerator in commodity cluster
computing for high-performance computing (HPC). For example,
three of the top five fastest supercomputers in the world, as
ranked by the TOP500, employ GPUs as accelerators. Despite this
increasing interest in GPUs, however, optimizing the performance
of a GPU-accelerated compute node requires deep technical
knowledge of the underlying architecture. Although significant
literature exists on how to optimize GPU performance on the
more mature NVIDIA CUDA architecture, the converse is true
for OpenCL on the AMD GPU.
Consequently, we present and evaluate architecture-aware optimizations
for the AMD GPU. The most prominent optimizations
include (i) explicit use of registers, (ii) use of vector types, (iii)
removal of branches, and (iv) use of image memory for global data.
We demonstrate the efficacy of our AMD GPU optimizations by
applying each optimization in isolation as well as in concert to
a large-scale, molecular modeling application called GEM. Via
these AMD-specific GPU optimizations, the AMD Radeon HD
5870 GPU delivers 65% better performance than with the wellknown
NVIDIA-specific optimizations
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