HP ProLiant DL288 Visualization and Acceleration in HP ProLiant servers - Page 4

Acceleration products Graphics processing units GPU's were originally developed to deliver the graphics rendering capabilities used in Visualization applications. Computer scientists and the companies that produced GPUs soon realized that the largescale parallel computing capability of GPUs could be harnessed to run general purpose computational applications, leading to the development of GPGPUs. Leading vendors such as NVIDIA® and AMD™-ATI began specializing their GPUs and creating programming environments to make them fully programmable for computational applications. In addition, they introduced programming support for high-level languages such as C. NVIDIA has created the CUDA parallel architecture and programming model and ATI has introduced Stream Computing using the AMD Compute Abstraction Layer (CAL) and Brook+ APIs. Each of these are proprietary programming architectures. Both AMD-ATI and NVIDIA plan to support OpenCL, an open standard C-based language for programming of GPUs, CPUs and other parallel processors. Field Programmable Gate Arrays Field programmable gate arrays (FPGAs) are semiconductor devices that can be programmed after they are manufactured to perform, in hardware, any set of logical functions. FPGAs contain programmable "logic blocks" and reconfigurable interconnects for wiring these blocks together to perform complex functions. They can be thought of as reconfigurable integrated circuits. FPGAs have been widely used for applications such as digital signal processing and embedded controllers. In recent years, several companies have taken advantage of the inherent parallelism of the logic resources on FPGAs to develop FPGA-based platforms that can be used to create customized Acceleration solutions. These platforms come with development systems that allow designers to define an Acceleration solution in which the key parts of the required computational processes are actually compiled into the FPGA. FPGA acceleration products for servers come in two different physical configurations. Some products use FPGAs on PCI-X or PCI Express cards that install in a system slot. Others consist of accelerator modules that plug directly into a processor socket on the system motherboard. FPGA-based acceleration solutions vendors include XtremeData™ , Nallatech™ and DRC as well as others. Comparing GPU and FPGA Acceleration Both GPU-based and FPGA-based acceleration offer unique and cost-effective solutions for supporting large-scale computationally intense applications on industry standard systems. In general, there is no one right answer; however, there are factors to consider when comparing the two technologies.  FPGA-based acceleration may be able to offer the best performance possible for specific Acceleration application appliances that do not require frequent changes.  Creating and modifying acceleration applications using GPU's and their development systems is generally easier than using FPGAs.  FPGA-based acceleration products tend to use less power than GPU's.  Because it is software-based, GPU-based acceleration does not require hardware re-programming to run a different acceleration application.  FPGAs work well on small objects such as characters or lower precision numbers (1 to 32 bit).  Current GPUs work well with 32 bit floating point precision as well as small objects. The next generation GPUs are expected to work well with 64 bit floating point precision. 4