GPU 2014 - International Workshop on Dependable GPU Computing

Many-core hardware accelerators offer significant speedup for parallel applications in different computing segments. Some are originally architected for general-purpose parallel computing while others, like Graphics Processing Units (GPUs), are initially designed for graphics applications, only. GPUs are intensively used today for general-purpose computing (GPGPU computing paradigm) through the employment of effective software development frameworks. GPUs and other accelerators have penetrated a very wide range of applications: from embedded and low-power devices to the highest performance super-computers.
This workshop focuses on an important aspect of GPUs and massively parallel hardware accelerators:
dependability and corresponding performance and power/energy considerations. GPUs and accelerators implemented in modern manufacturing technologies are vulnerable (like all other chips) to transient as well as to permanent faults due to radiation, manufacturing defects, variability, aging, etc. In general-purpose computing, the correctness of operation has a much higher priority than in graphics and the applications are expected to deliver correct and fast results. Such dependability provision can be realized at the circuit level, the architectural level, the software level or a combination. However, dependability support significantly affects: (a) the delivered performance of the application, and (b) its power/energy consumption behavior.
Topics to be discussed in the workshop include (but are not limited to) the following:
Dependability requirements from GPUs and accelerators in different application domains: embedded computing vs. high-performance computing, low-power devices vs. large-scale systems, scientific computing vs. graphics processing and gaming.
Effective and reliable use of GPUs and accelerators in the various fields of electronic design automation among others.
Experimental evaluation, measurements and case studies for GPUs and accelerators dependability.
Dependability enhancement methodologies (software-based, hardware-based or mixed) for GPUs and accelerators.
Performance penalty, hardware cost, and power/energy overhead of dependability support for GPUs and accelerators.
CPU vs. GPU tolerance comparison against transient or permanent hardware faults.