White paper sponsored by: Next-Generation Data Center Architecture for Advanced Compute and Visualization in Upstream Oil and Gas

Transcription

1 White paper sponsored by: Next-Generation Data Center Architecture for Advanced Compute and Visualization in Upstream Oil and Gas

2 TABLE OF CONTENTS 1 EXECUTIVE SUMMARY INTRODUCTION TRADITIONAL APPROACH TO INFRASTRUCTURE: CHALLENGES AND OPPORTUNITIES BRINGING GPUS INSIDE THE DATA CENTER REMOTE VISUALIZATION INTEGRATION OF VIRTUALIZATION AND CLOUD TECHNOLOGIES CONCLUSION REFERENCES... 8

3 September 2012 Next-Generation Data Center Architecture for Advanced Compute and Visualization in Upstream Oil and Gas 1 EXECUTIVE SUMMARY Computing infrastructure for upstream oil and gas has evolved within siloed IT environments to satisfy unique requirements or because of boundaries between various groups created by factors such as a lack of integration between different scientific disciplines and immature software. Visualization has typically been separate from data center environments, with visualization systems located close to end users who need them. Today, upstream workflows are becoming more integrated, revealing weaknesses in the traditional model. For instance, seismic processing and interpretation are no longer separate steps; they are performed in a loop. Interpreters visualize intermediate results to direct seismic processing jobs. Where a project might have consisted of two or three processing jobs in the past, it now may require 20 to 30 jobs. At the same time, graphics processing unit (GPU) computing is increasingly important for the acceleration of various upstream computational kernels. Recent developments, including the release of the NVIDIA Kepler GPU architecture and the NVIDIA VGX platform for remote visualization and GPU virtualization will further accelerate this trend. As a result, visualization and computing infrastructure (GPUs and CPUs) need to be closer to each other than in the past. Data growth which in some cases is exponential is making it almost essential for visualization, compute, and data resources to be colocated. Bringing GPUs inside the data center. The adoption of GPU computing, in conjunction with the emergence of flexible server and storage virtualization and rapidly maturing cloud computing technologies, makes a strong case for reconsidering the infrastructure supporting high-performance computing and visualization in an integrated, next-generation data center design in which GPUs are brought inside the upstream oil and gas data center. Colocating GPUs, central processing units (CPUs), and data avoids many of the data bandwidth, data management, and other challenges associated with dispersed visualization systems, while allowing computing resources to be fully used at all times for a combination of visual applications and highperformance computing (HPC) applications. The ability to virtualize GPU resources analogous to the way server virtualization solutions virtualize CPU resources will make it possible to provide a flexible pool of GPUs that are available for both visualization and computation needs. With these new capabilities, virtualization can be extended to all type of applications. Remote visualization. For oil and gas companies, mobility is a lot more than a nice to have capability it is the nature of the business. Remote, interactive 3D visualization technologies have been pursued for many years and today are finally able to deliver a solid combination of high-resolution, high-performance, and low required bandwidth to support the geographically dispersed nature of oil and gas. Advances, including the ability to orchestrate and manage the use of hybrid CPU/GPU clusters and GPU integration into hypervisors, will allow a wider range of end users to take advantage of real-time visualization using less specialized displays, supporting geographically dispersed collaboration, and speeding up decision making while allowing datasets to grow and remain centralized. Remote visualization will not immediately replace all high-end desktop systems, but it will complement them to enable remote workers to have full access to the same IT resources they can access from inside the company. It will also enable specialized cloud providers to further augment a company s own IT infrastructure. Integrating the upstream oil and gas data center with the cloud. Virtualization and cloud technology will make it possible to integrate and dynamically share all the compute, GPU, networking, and storage resources necessary for computation, visualization, and interpretation as part of a next-generation upstream oil and gas data center. These new data centers will deliver greater flexibility, efficiency, and economies of scale while improving computational performance, optimizing data management, and facilitating the use of critical visualization capabilities by experts and decision makers. Ultimately, this approach will facilitate a seamless connection between local and remote facilities, allowing an oil and gas data center to use remote IT resources either at secondary locations or in public clouds as necessary to satisfy compute and visualization needs. This new IT infrastructure will also lead to further changes in workflows 1

4 and practices for better collaboration, faster deployment of new joint venture projects, and provisioning of advanced IT capabilities to remote operations. This white paper explores these emerging trends in detail, incorporating the latest ideas from industry leaders such as Paradigm, NVIDIA, NetApp, Cisco, and NICE. 2 INTRODUCTION The rapid growth in dataset sizes and the integration of large numbers of diverse, multidisciplinary data types to more accurately understand the earth s subsurface have contributed to the continued evolution of seismic processing, reservoir simulation, interpretation, visualization, digital oil field, and petroleum economics technologies. Emerging trends in the IT industry are creating an opportunity to rethink traditional approaches to upstream oil and gas data centers to better manage the data explosion, and also to facilitate global collaboration and to speed interpretation by using cloud computing technologies. Through in-depth discussions with experts from both the computer and oil and gas industries, the authors of this paper sought to understand: Limitations of the current infrastructure Pressures that are driving change in upstream data centers Potential impact of IT trends, including GPU-based processing, advances in remote visualization, and the rapid development of virtualization and cloud technologies This white paper discusses these issues, details the current state of the art as practiced by several industry leaders, and provides insight into the architecture of next-generation oil and gas data centers. 3 TRADITIONAL APPROACH TO INFRASTRUCTURE: CHALLENGES AND OPPORTUNITIES The computing and data storage infrastructure used in upstream oil and gas data centers has typically been centered around discrete, siloed architectures designed to meet the particular needs of seismic processing, reservoir simulation, and interpretation. Advanced visualization needs have been addressed by high-powered graphics workstations or other visualization systems located outside the data center. As the scale and complexity of oil and gas projects continue to grow, the limitations are increasingly obvious. Specialized silos of IT infrastructure lack flexibility. Expensive resources cores, network bandwidth, storage capacity, and I/O may sit idle in one silo, while another environment is hamstrung and suffers delays. Provisioning each environment separately to meet peak demand is both expensive and inefficient. With dataset sizes growing exponentially, feeding data to visualization workstations outside the data center is increasingly difficult. Network connections lack the necessary bandwidth for real-time operations, and copying data to local storage is prohibitive in terms of both the time needed for the copy and the additional storage required. Having multiple copies of valuable datasets especially copies outside the data center also creates significant security concerns and data management and governance issues. Changes in the way that upstream work is performed have created a need for visualization capabilities to facilitate quick visual inspection of results during computation and to meet the needs of a distributed workforce that can include collaborators (who may also be competitors) and people working at home and in the field. At the same time, new hybrid computing systems that do computation and visualization by using both CPU and GPU resources together are becoming mainstream, creating a need for GPU resources across numerous application environments. Future upstream data center architectures will address these challenges by using a combination of approaches: GPUs will move into the upstream data center, providing computation and visualization capabilities. Remote visualization technology will change the nature of visualization workstations. High-resolution displays will still be required, but the need for CPU and GPU resources on the workstation will be reduced. Workstations will be augmented and integrated with other devices such as tablets. Handheld devices will access centralized server applications via applets. Virtualization and cloud technology will allow upstream data centers to create private clouds in which resources can be dynamically allocated. Upstream data centers will supplement dedicated resources with compute resources in the public cloud. These initiatives are already under way. Leading-edge data centers are employing some or all of these technologies and are poised to increase adoption as the necessary technologies mature. 4 BRINGING GPUS INSIDE THE DATA CENTER The next-generation oil and gas data center will harness GPUs for two purposes: computing and remote visualization. This section focuses on computing uses; section 5 discusses visualization. By 2003, computer scientists and researchers began to recognize that the parallel processing capabilities of the GPU could be leveraged to accelerate computation for a wider range of problems. This approach is known as GPU computing or general-purpose computing on GPU (GPGPU). In many cases, the speedup observed can be 10-fold or greater with respect to specific computational kernels. (Pre- and postprocessing around these kernels continues to be done by CPUs.) GPUs have an inherently parallel design with higher data bandwidth and hundreds of small cores capable of executing concurrent threads. These cores can be used to process arrays of data elements such as those in a seismic survey in the same way that arrays of pixels are manipulated. GPU computing can be used to accelerate almost any application that can be parallelized. In 2007, NVIDIA introduced the Compute Unified Device Architecture (CUDA) to simplify programming parallel applications on its GPUs. CUDA provides C, C++, and Fortran extensions that eliminate the need to program GPUs by using unfamiliar graphics processing languages. This key development in the evolution of GPU computing is making much wider adoption possible, and GPU computing is now used in a variety of industries, including oil and gas, where NVIDIA is the dominant GPU vendor. In 2011, NVIDIA announced the OpenACC initiative, to enable intelligent compiler vendors such as PGI, CAPS, and 2

5 OpenMP to use compiler directives to automate the GPU parallelization of applications, extending the applicability of GPU computing to existing applications. Even applications such as MathLab and Microsoft Excel now have GPU extensions. In 2012, NVIDIA announced the NVIDIA Kepler computing architecture, optimized to address the GPU computing needs of seismic processing algorithms such as reverse time migration, full waveform inversion, and Kirchoff time/depth migration, and to enable the use of GPU computing for reservoir simulation. New capabilities like Dynamic Parallelism allow threads executing on a GPU to dynamically spawn new threads without CPU involvement, while Hyper-Q allows multiple CPU cores to simultaneously use the cores on a single Kepler GPU. Early adopters report that seismic applications run up to 1.8 times faster on Kepler than on the previous generation NVIDIA GPU with the same power consumption. In addition to raw performance and energy efficiency, Kepler is also designed for virtualization and low-latency remote display. A number of papers that discuss the use of GPU computing in seismic processing and interpretation are included in the reference section of this paper. GPU Computing in Oil and Gas. In 2011, there were 14 new GPU supercomputers on the top 500 list for oil and gas alone. For example, WesternGeco, Petrobras, Hess, Total, and Chevron are using GPUs for seismic processing. WesternGeco recently disclosed that they have more than 15,000 NVIDIA GPUs in production in their data centers. It has taken some time for oil and gas companies to fully invest in GPU technology because of GPU memory limitations and barriers created by programming difficulties. The OpenACC specification for directivebased programming of GPUs helps resolve the programmability issue and further promotes hybrid compute environments that contain both CPUs and GPUs. NVIDIA and other GPU providers are moving toward colocation of GPUs and CPUs on a processor board, taking full advantage of evolving PCI technology and exploiting data streaming techniques. In the future, CPUs and GPUs are likely to be built into a single chip. GPUs are also being used in reservoir simulation and modeling as well as petroleum economics and energy trading. The significant power reduction (factor of 10) per GPU GFLOP versus CPUs makes them attractive to deploy for these applications. GPU Computing: An ISV Perspective. Industry trends, such as the increased use of prestack seismic data analysis in interpretation workflows and the larger dataset sizes associated with modern seismic acquisition programs, continue to create a compute intensive environment where GPU based computing can be applied. In the HPC arena, seismic processing and imaging algorithms such as full wave migration, reverse time migration and surface multiple elimination are very compute intensive and can benefit from the additional compute capabilities provided by GPU computing. The appearance of compute servers in the marketplace that are specifically built to accommodate GPUs for HPC computing was clear evidence of demand for this type of compute capability and has helped to create a marketplace for HPC software that is GPU compute capable. For desktop applications, adoption of GPU computing has focused on improving performance for compute-intensive tasks to improve interactivity. For example, in the SKUA subsurface modeling program GPU based computing has been used to speed up the matrix inversion used in the innovative UVT Transform. GPU based computing has also been used to improve the quality of the volume rendering display in Voxel- Geo, helping users refine their understanding of the variations in the seismic images of the subsurface. These focused implementations of GPU computing have an immediate impact on users ability to locate or rank prospects, and help to improve productivity while working under tight deadlines. The availability of professional series graphics cards in many E&P desktop and blade workstations facilitates use of OpenGL based 3D graphics but requires branching code in order to support GPU based computing in E&P desktop applications. Any investment in branching code must consider the value of that effort to the work being performed by the end user of the software. The availability and maturity of the software development tools and libraries that support accelerator based computing are also important factors in deciding when and how to commit to a particular implementation. 5 REMOTE VISUALIZATION The second advantage of bringing GPUs inside the data center is the ability to use remote visualization technologies to supplement or replace the capabilities of specialized visualization hardware used for high-end 3D and 4D processing and delivering graphical output to remote clients. For oil and gas, mobility is the nature of the business. Remote, interactive 3D visualization technologies for high-end interpretation have been pursued for many years and today are maturing to deliver an excellent level of performance, even for high-end 3D visualization tasks. Remote visualization will improve the use of visualization in oil and gas exploration and production in a number of ways: Make visualization available when and where it s needed Improve collaboration Facilitate workflows that cross organizational boundaries Overcome problems created by dataset size and data sensitivity By centralizing GPU resources in the upstream data center and using remote visualization capabilities, the results of visualization can be made available where they re needed, whether that s near the data center, remotely, or in the field. Collaborators in different locations view and manipulate the same images, eliminating potential points of confusion and miscommunication while saving valuable time. Data stays in the data center, where it s protected, without the need for time-consuming copies. In cases where a dataset cannot legally leave the country, remote visualization facilitates both analysis and collaboration that might not be possible otherwise. It s important to understand that remote visualization doesn t always mean that long distances are involved. The visualization application is remote from the user, but in the majority of cases, the user may be relatively close to the data center, connected via an intranet with good bandwidth and low latency. Quick collaboration no longer requires multiple users to gather around the same workstation. Intermediate analysis or computational steering, in which a user visualizes data during processing to guide the result, is also accelerated. Remote Visualization Quality. The quality of remote visualization comes down to 3

6 Figure 1) Next-generation data center with shared infrastructure to meet the needs of multiple types of users. (Monitor images courtesy of Paradigm)

7 three factors: resolution (pixels), bandwidth, and latency. Screen resolution of 1920X1200 typically requires from 8-32Mbps bandwidth. Latency less than 30 milliseconds typically results in no compromise in user experience, and less than 80 milliseconds yields a good experience. Because low latency is essential to the quality of a remote visualization experience, NVIDIA has made a significant effort to squeeze the latency out of its graphics products. The recently announced NVIDIA Kepler architecture and VGX platform deliver fully compressed frames from an NVIDIA Kepler GPU to an IP stack faster than to a local monitor, ensuring that total latency is affected only by network conditions. Remote Visualization Software Desktop Cloud Visualization (DCV) RGS XenDesktop HDX 3D Pro ThinAnywhere (TAw) Because the technology works at the driver level, visualization applications are able to take advantage of it with no modifications. An ISV can test an application locally and be guaranteed that it will work remotely (assuming that bandwidth and latency minimums are met). Because latency is directly related to distance, regional hubs capable of providing remote visualization services with good latency to workers in each region may be necessary in larger oil and gas companies. Visualization in the oil and gas field poses some unique quality requirements. Both color and crispness of edges are important, and images must be free of artifacts. NICE Desktop Cloud Visualization (DCV), for example, has been optimized to meet these requirements. DCV offers dynamic quality adjustment. During mouse movements, compression rates increase to provide smoother motion; when movement stops, DCV can deliver high-quality or lossless still images for maximum resolution without artifacts. Figure 2) VGX Remote Display reduces latency by bypassing much of the typical graphics stack. Remote Visualization at Eni. Eni is an integrated energy company that recognizes the value of remote visualization and has created a strategy to consolidate both batch and interactive applications on a centralized infrastructure. The Eni technical cloud has a hub in Houston that focuses on visualization, while the main data center in Milan handles the bulk of European visualization and HPC worldwide. Eni has been using its remote visualization approach since 2008 and has seen major advantages, including significant cost savings resulting from the elimination of expensive graphics workstations in favor of lower-cost desktops serving as thin clients. Vendor NICE HP Citrix ThinAnywhere Table 1) Remote visualization software that supports NVIDIA technology. The typical desktop is now a PC with dual monitors and no specialized software installed locally, significantly reducing the amount of desktop IT support needed. Overall efficiency in terms of hardware and software utilization has gone up. Remote visualization facilitates work by virtual teams and makes it easier to bring the most appropriate skill sets to bear on complex problems. All user sessions begin with the user connecting to a portal created by using NICE EnginFrame. This portal lets users see available datasets and select the application services they want. Individual interactive services are accessed via multiple remote desktop protocols, with NICE DCV handling the majority of technical 3D sessions. When a session is requested, the scheduler identifies the appropriate resources based on the type of job (batch or interactive), the particular application, and the user s specific profile. With EnginFrame, users are able to see and manage their active sessions, including the ability to easily share a session for collaboration. Another advantage is that the user can be immediately productive from anywhere the portal can be accessed. Rather than maintain a homogeneous cluster of servers with the same CPU, GPU, and memory resources, Eni maintains pools of various server configurations with differing capabilities tuned to meet the requirements of a wide variety of oil and gas applications. Remote Visualization: An ISV Perspective. The availability of remote visualization solutions that can effectively support the complex OpenGL based 3D graphics displays that are used extensively in E&P software are a critical element of the next generation data center. In order to be accepted as a replacement for a local workstation by end users, a data center based solution must meet the usability requirements of the end user. It must support the display of the complex 3D graphics without loss of fidelity, it must support the variety of monitor displays including multiple 30 inch 2560x1600 resolution monitors, and it must do so without negatively 5

8 impacting the user s interactivity with the software. The efforts in the reduction of latency in remote visualization offerings, which builds on demand from different industries, will be extremely beneficial to the E&P user community. Some of the display techniques used for visualizing seismic data, such as opacity based rendering, are quite demanding, not only in terms of the 3D graphics operations themselves. They also result in extensive changes to the images being displayed, which in turn may impact the capability of remote visualization software where image compression/decompression and encryption/decryption are used. The network to the thin clients in offices must be able to support the network traffic associated with the remote visualization solution. There are a number of benefits associated with moving the E&P workstations back into the data center, such as a reduction in the cooling and power requirements in offices, the increased data I/O rates available in the computer center for loading data into systems, and improved facilities management such as the time required to set up new users or to move users into new offices. Standard PC users who use 2D graphics have already benefited from data center consolidation and virtualization that is enabling a reduction in the ratio of PCs to users. The forthcoming virtualization of GPUs will offer similar benefits of resource pooling for the E&P 3D graphics user community. Many remote visualization solutions offer additionally capabilities such as an improved collaboration environment. Multidisciplinary professionals can share the same visualization, leading to a demand for software that integrates the display of diverse data types, including data that is sampled at different scales: seismic scale, core scale, borehole scale, and microseismic scale. 6 INTEGRATION OF VIRTUALIZATION AND CLOUD TECHNOLOGIES The Eni example discussed in the previous section can be considered a private cloud deployment. Users in this environment don t need to concern themselves with the underlying hardware; resources are identified and assigned to each job automatically based on requirements. The infrastructure is designed to meet particular needs while increasing efficiency and providing greater flexibility than traditional approaches. Figure 3) GPU virtualization in NVIDIA VGX. Although the technologies used to create this private cloud are far from those used in mainstream IT, over time more mainstream technologies will converge with the needs of oil and gas to become a part of the upstream data center. Many upstream data centers have already moved to incorporate standard server virtualization technologies. For example, some applications rely on a set of services that provide security and other functions; those services need to be available for applications to run. Many users have found that virtualizing those services improves uptime and allows them to be migrated without downtime. Virtualization of core visualization applications, however, has been hampered by the inability to virtualize GPU resources. GPU Virtualization. The Eni scenario works at the granularity of physical servers or server blades (and their associated GPU resources). With servers capable of supporting up to 16 GPUs already in existence, the ability to virtualize and share GPU resources in a manner analogous to CPU resources is becoming increasingly desirable for both applications that use modest GPU computing resources (like reservoir simulation codes) and for remote visualization. GPU computing applications such as seismic processing that require hundreds or thousands of GPUs will continue to be best served by physical hardware. VMware, a server virtualization leader, has demonstrated GPU computing performance at 98% of native performance when using a single GPU from within a virtual machine (VM) using both CUDA and OpenCL. VMware and others plan to incorporate the NVIDIA VGX platform into their products, to enable virtual desktop infrastructure to support remote 3D visualization. The NVIDIA VGX platform is designed to enable GPU virtualization by using three key technologies: VGX boards provide multiple Kepler GPUs in a standard server PCI Express slot. The VGX hypervisor integrates into commercial hypervisors to enable GPU virtualization. User-selectable machines allow graphics capabilities to be preconfigured to support the needs of different types of users. Citrix XenDesktop will support VGX capabilities as they become available. XenDesktop HDX 3D Pro supports VGX performance acceleration now and is ready to support virtualization capabilities as they are released. This will allow users who require occasional access to graphics-intensive applications to access them from any device. NICE DCV today provides GPU sharing for both Windows and Linux applications, and can even allow the use of GPU appliances network-attached GPU servers that allow VMs to accelerate OpenGL applications over a standard gigabit network. Evolving Next-Generation Private Cloud Infrastructure. NVIDIA and Cisco have partnered to architect a next-generation infrastructure capable of supporting both physical and virtualized hardware 6

9 resources. The work they are doing will leverage the FlexPod data center platform developed by Cisco and NetApp to help customers accelerate the deployment of shared infrastructure and the cloud. FlexPod combines NetApp storage with Cisco network and compute capabilities into a converged infrastructure that accelerates and simplifies infrastructure deployment and management. The idea is to create an easy-to-deploy infrastructure that is capable of flexibly supporting a range of geological and geophysical applications, including those from Paradigm, Schlumberger, and Halliburton. Users will be able to request a session of a particular type, and the appropriate resources either physical or virtual will be provisioned automatically. A single rack system will include: Cisco UCS C-Series rack mount servers NextIO PCI Express enclosure NVIDIA VGX boards (4 GPUs per board) Cisco Nexus and Cisco UCS infrastructure (full 10Gigabit Ethernet fabric) NetApp FAS storage Cisco C-Series servers connect via host interface cards to GPUs in external PCI Express enclosures. The optimal number of GPUs per server is still to be determined; available hardware currently supports up to 16 GPUs per server. The network infrastructure supports a full 10Gigabit Ethernet internal fabric for serverto-storage and server-to-server communication. Remote direct memory access provides the low-latency connectivity required between clustered servers. External connections to clients can use either gigabit or 10Gigabit Ethernet. Figure 4) Validated solutions reduce risk and expense and speed deployment while providing the flexibility to adapt to changing needs. NetApp FAS storage stores user datasets and provides SAN and NAS access, depending on application requirements. NetApp delivers unified storage performance along with proven storage efficiency technologies, including deduplication and compression, and integrated data protection to accelerate both backup and recovery in oil and gas application environments. Cisco Intelligent Automation for Cloud will provide orchestration for the infrastructure. The software will automatically provision both clusters of bare metal servers (with attached GPU resources) for HPC and multiple virtual machines, each with a single GPU, for interactive sessions via remote visualization. This will allow the solution to provide for example bare metal server clusters running Linux for reservoir simulation and VMs with GPU resources running Windows to support interactive seismic interpretation sessions with remote visualization. At night, when interactive use declines or stops, associated resources can be dynamically reprovisioned for GPU-computing jobs, making very efficient use of all the available resources. Transitioning to Cloud. Some companies are skeptical of cloud technology because of concerns that the resources to accomplish a particular task won t be available when needed. Although virtualization and cloud technologies allow you to more flexibly allocate and reallocate resources as needs change, the changes that you make on a day-to-day basis don t need to be extensive. You can allocate resources for important tasks for as long as necessary even more or less permanently. The difference is that the building blocks you use will be more standardized, making procurement and deployment easier, and you ll be able to make better use of resources. Rather than having unused resources allocated in each application silo, a single pool of resources can meet unexpected growth or peaks in demand in different areas. Where Does Public Cloud Make Sense? Although most oil and gas data centers focus on the potential benefits of private cloud, public cloud options may make sense for a variety of scenarios. A number of public cloud providers already offer GPU and other HPC resources, including Amazon EC2, Nimbix, Peer1, and Penguin Computing. The availability of both GPU and CPU resources in the cloud opens the possibility of using public cloud resources as an adjunct to or instead of internal infrastructure. The biggest limitation in this regard is data. Many companies would have considerable concern about uploading critical data to a public cloud, and bandwidth limitations would probably make it impossible to use public cloud resources with data stored internally. Also, simply getting data into the public cloud could be prohibitively time consuming. Companies that offer HPC cloud services recognize these concerns and do as much as possible to mitigate them, offering services such as private clouds within their infrastructure that are walled off from other users, as well as high-speed networking infrastructure for moving data. Not all upstream projects are massive in size, and some projects need to be done in geographic locations where size, local skills, 7

10 latency, and connectivity present serious obstacles. In such cases, using public cloud resources (if available) may be the best option. Public cloud also may make business sense for quick hit projects small, shared projects that last 30 to 60 days especially those that involve outside players. 7 CONCLUSION The incorporation of GPUs in the oil and gas data center must address three scenarios: Compute clusters with tens to hundreds or thousands of GPUs More modest GPU computing with 2 to 10 GPUs Remote visualization with 1 GPU per visualization session In the near term, upstream data centers will be able to begin moving away from silos of infrastructure by deploying standardized, multi-gpu servers suitable for both computation and visualization and incorporating them into daily operations by using technologies similar to those used by Eni or under development by technology leaders such as NVIDIA, NICE, Cisco, and NetApp. In the longer term, mainstream virtualization technologies will better incorporate GPUs, resulting in more options to address GPU computing and remote visualization scenarios and to achieve increased automation via private cloud. Converged infrastructure solutions such as the FlexPod data center platform will incorporate GPU resources to address highend 3D remote visualization and more modest GPU computing requirements. Such solutions integrate necessary infrastructure elements and provide uniform building blocks to simplify and accelerate infrastructure deployment. n 8 REFERENCES HPC in Oil and Gas Exploration. Anthony Lichnewsky. Presentation at Prace 2011 Industry Workshop: /pdf/schlumberger_alichnewsky_is_2 011.pdf Leveraging Graphics Processing Units (GPUs) for Real-time Seismic Interpretation. Benjamin J. Kadlek and Geoffrey A. Dorn. The Leading Edge, January 2010: Maximizing Throughput for High Performance TTI-RTM: From CPU-RTM to GPU-RTM. Xinyi Sun and Sang Suh. SEG San Antonio 2011 Annual Meeting: Expanding Domain Methods in GPU Based TTI Reverse Migration. Sang Suh and Bin Wang. SEG San Antonio 2011 Annual Meeting: Giga-Cell Simulation. Dr. Ali H. Dogru. Saudi Aramco Journal of Technology, Spring 2011: Spring2011/GigaCell.pdf Energy efficient resource allocation strategy for cloud data centres. Domenico Sannelli, Federico Mezza, Eni. International Symposium on Computer and Information Sciences, September 2011: Santos sponsors Open Source software for better reservoir visualization. Featured article in Finding Petroleum, March 2011: Source_ software_for_better_reservoir_visualization/69e8c2de.aspx 8