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Domain-specific languages are a useful tool for productivity allowing domain experts to program using familiar concepts and vocabulary while benefiting from performance choices made by computing experts. Embedding the domain specific language into an existing language allows easy interoperability with non-domain-specific code and use of standard compilers and build systems. In C++, this is enabled through the template and preprocessor features. C++ embedded domain specific languages (EDSLs) allow the user to write simple, safe, performant, domain specific code that has access to all the low-level functionality that C and C++ offer as well as the diverse set of libraries available in the C/C++ ecosystem. In this paper, we will discuss several tools available for building EDSLs in C++ and show examples of projects successfully leveraging EDSLs. Modern C++ has added many useful new features to the language which we have leveraged to further extend the capability of EDSLs. At EM Photonics, we have used EDSLs to allow developers to transparently benefit from using high performance computing (HPC) hardware. We will show ways EDSLs combine with existing technologies and EM Photonics high performance tools and libraries to produce clean, short, high performance code in ways that were not previously possible.

When capturing image data over long distances (0.5 km and above), images are often degraded by atmospheric turbulence, especially when imaging paths are close to the ground or in hot environments. These issues manifest as time-varying scintillation and warping effects that decrease the effective resolution of the sensor and reduce actionable intelligence. In recent years, several image processing approaches to turbulence mitigation have shown promise. Each of these algorithms have different computational requirements, usability demands, and degrees of independence from camera sensors. They also produce different degrees of enhancement when applied to turbulent imagery. Additionally, some of these algorithms are applicable to real-time operational scenarios while others may only be suitable for post-processing workflows. EM Photonics has been developing image-processing-based turbulence mitigation technology since 2005 as a part of our ATCOM [1] image processing suite. In this paper we will compare techniques from the literature with our commercially available real-time GPU accelerated turbulence mitigation software suite, as well as in-house research algorithms. These comparisons will be made using real, experimentally-obtained data for a variety of different conditions, including varying optical hardware, imaging range, subjects, and turbulence conditions. Comparison metrics will include image quality, video latency, computational complexity, and potential for real-time operation.

The OpenCL API provides an abstract mechanism for massively parallel programming on a very wide range of hardware, including traditional CPUs, GPUs, accelerator devices, FPGAs, and more. However, these different hardware architectures and platforms function quite differently. Therefore, coding OpenCL applications that are usefully portable is challenging. Certain considerations are therefore required in developing an effectively portable OpenCL library to enable parallel application development without requiring fully separate code paths for each target platform. By making use of device detection and characterization provided by the OpenCL API, valuable information can be obtained to make runtime decisions for optimization. In particular, the effects of memory affinity change depending on the memory organization of the device architecture. Work partitioning and assignment depend on the device execution model, in particular the types of parallel execution supported and available synchronization primitives. These considerations, in turn, affect the selection and invocation of kernel code. For certain devices, platform-specific libraries are available, while others can benefit from generated kernel code based on the specified device parameters. By parameterizing an algorithm based on how these considerations affect performance, a combination of device parameters can be used to produce an execution strategy that will provide improved performance for that device or collection of devices.

Many ISR applications require constant monitoring of targets from long distance. When capturing over long distances, imagery is often degraded by atmospheric turbulence. This adds a time-variant blurring effect to captured data, and can result in a significant loss of information. To recover it, image processing techniques have been developed to enhance sequences of short exposure images or videos in order to remove frame-specific scintillation and warping. While some of these techniques have been shown to be quite effective, the associated computational complexity and required processing power limits the application of these techniques to post-event analysis. To meet the needs of real-time ISR applications, video enhancement must be done in real-time in order to provide actionable intelligence as the scene unfolds. In this paper, we will provide an overview of an algorithm capable of providing the enhancement desired and focus on its real-time implementation. We will discuss the role that GPUs play in enabling real-time performance. This technology can be used to add performance to ISR applications by improving the quality of long-range imagery as it is collected and effectively extending sensor range.

Several image processing techniques for turbulence mitigation have been shown to be effective under a wide range of long-range capture conditions; however, complex, dynamic scenes have often required manual interaction with the algorithm’s underlying parameters to achieve optimal results. While this level of interaction is sustainable in some workflows, in-field determination of ideal processing parameters greatly diminishes usefulness for many operators. Additionally, some use cases, such as those that rely on unmanned collection, lack human-in-the-loop usage. To address this shortcoming, we have extended a well-known turbulence mitigation algorithm based on bispectral averaging with a number of techniques to greatly reduce (and often eliminate) the need for operator interaction. Automations were made in the areas of turbulence strength estimation (Fried’s parameter), as well as the determination of optimal local averaging windows to balance turbulence mitigation and the preservation of dynamic scene content (non-turbulent motions). These modifications deliver a level of enhancement quality that approaches that of manual interaction, without the need for operator interaction. As a consequence, the range of operational scenarios where this technology is of benefit has been significantly expanded.

The OpenCL standard for general-purpose parallel programming allows a developer to target highly parallel computations towards graphics processing units (GPUs), CPUs, co-processing devices, and field programmable gate arrays (FPGAs). The computationally intense domains of linear algebra and image processing have shown significant speedups when implemented in the OpenCL environment. A major benefit of OpenCL is that a routine written for one device can be run across many different devices and architectures; however, a kernel optimized for one device may not exhibit high performance when executed on a different device. For this reason kernels must typically be hand-optimized for every target device family. Due to the large number of parameters that can affect performance, hand tuning for every possible device is impractical and often produces suboptimal results. For this work, we focused on optimizing the general matrix multiplication routine. General matrix multiplication is used as a building block for many linear algebra routines and often comprises a large portion of the run-time. Prior work has shown this routine to be a good candidate for high-performance implementation in OpenCL. We selected several candidate algorithms from the literature that are suitable for parameterization. We then developed parameterized kernels implementing these algorithms using only portable OpenCL features. Our implementation queries device information supplied by the OpenCL runtime and utilizes this as well as user input to generate a search space that satisfies device and algorithmic constraints. Preliminary results from our work confirm that optimizations are not portable from one device to the next, and show the benefits of automatic tuning. Using a standard set of tuning parameters seen in the literature for the NVIDIA Fermi architecture achieves a performance of 1.6 TFLOPS on an AMD 7970 device, while automatically tuning achieves a peak of 2.7 TFLOPS

The OpenCL API allows for the abstract expression of parallel, heterogeneous computing, but hardware implementations have substantial implementation differences. The abstractions provided by the OpenCL API are often insufficiently high-level to conceal differences in hardware architecture. Additionally, implementations often do not take advantage of potential performance gains from certain features due to hardware limitations and other factors. These factors make it challenging to produce code that is portable in practice, resulting in much OpenCL code being duplicated for each hardware platform being targeted. This duplication of effort offsets the principal advantage of OpenCL: portability. The use of certain coding practices can mitigate this problem, allowing a common code base to be adapted to perform well across a wide range of hardware platforms. To this end, we explore some general practices for producing performant code that are effective across platforms. Additionally, we explore some ways of modularizing code to enable optional optimizations that take advantage of hardware-specific characteristics. The minimum requirement for portability implies avoiding the use of OpenCL features that are optional, not widely implemented, poorly implemented, or missing in major implementations. Exposing multiple levels of parallelism allows hardware to take advantage of the types of parallelism it supports, from the task level down to explicit vector operations. Static optimizations and branch elimination in device code help the platform compiler to effectively optimize programs. Modularization of some code is important to allow operations to be chosen for performance on target hardware. Optional subroutines exploiting explicit memory locality allow for different memory hierarchies to be exploited for maximum performance. The C preprocessor and JIT compilation using the OpenCL runtime can be used to enable some of these techniques, as well as to factor in hardware-specific optimizations as necessary.

We examine the performance of a commercially available speckle imaging system in reconstructing static scenes from imagery corrupted by anisoplanatic distortions commonly observed when imaging over long horizontal paths near the ground. Performance is evaluated using the Mean Squared Error between system outputs and a diffraction-limited reference image. Input image frames are taken from a large library of simulated imagery of a static object observed over a 1 km horizontal path through volume turbulence in 3 turbulence conditions. 1000 image frames are available for each condition allowing for a statistically significant characterization of system performance over a range of turbulence conditions.

Imaging scenarios commonly involve erratic, unpredictable camera behavior or subjects that are prone to movement, complicating multi-frame image processing techniques. To address these issues, we developed three techniques that can be applied to multi-frame image processing algorithms in order to mitigate the adverse effects observed when cameras are panning or subjects within the scene are moving. We provide a detailed overview of the techniques and discuss the applicability of each to various movement types. In addition to this, we evaluated algorithm efficacy with demonstrated benefits using field test video, which has been processed using our commercially available surveillance product. Our results show that algorithm efficacy is significantly improved in common scenarios, expanding our software’s operational scope. Our methods introduce little computational burden, enabling their use in real-time and low-power solutions, and are appropriate for long observation periods. Our test cases focus on imaging through turbulence, a common use case for multi-frame techniques. We present results of a field study designed to test the efficacy of these techniques under expanded use cases.

Located at Edwards Air Force Base, Armstrong Flight Research Center (AFRC) is NASA’s premier site for aeronautical research and operates some of the most advanced aircraft in the world. As such, flight tests for advanced manned and unmanned aircraft are regularly performed there. All such tests are tracked through advanced electro-optic imaging systems to monitor the flight status in real-time and to archive the data for later analysis. This necessitates the collection of imagery from long-range camera systems of fast moving targets from a significant distance away. Such imagery is severely degraded due to the atmospheric turbulence between the camera and the object of interest. The result is imagery that becomes blurred and suffers a substantial reduction in contrast, causing significant detail in the video to be lost. In this paper, we discuss the image processing techniques located in the ATCOM software, which uses a multi-frame method to compensate for the distortions caused by the turbulence.

EM Photonics has been investigating the application of massively multicore processors to a key problem area: Computational Fluid Dynamics (CFD). While the capabilities of CFD solvers have continually increased and improved to support features such as moving bodies and adjoint-based mesh adaptation, the software architecture has often lagged behind. This has led to poor scaling as core counts reach the tens of thousands. In the modern High Performance Computing (HPC) world, clusters with hundreds of thousands of cores are becoming the standard. In addition, accelerator devices such as NVIDIA GPUs and Intel Xeon Phi are being installed in many new systems. It is important for CFD solvers to take advantage of the new hardware as the computations involved are well suited for the massively multicore architecture. In our work, we demonstrate that new features in NVIDIA GPUs are able to empower existing CFD solvers by example using AVUS, a CFD solver developed by the Air Force Research Labratory (AFRL) and the Volcanic Ash Advisory Center (VAAC). The effort has resulted in increased performance and scalability without sacrificing accuracy. There are many well-known codes in the CFD space that can benefit from this work, such as FUN3D, OVERFLOW, and TetrUSS. Such codes are widely used in the commercial, government, and defense sectors.

The modern graphics processing unit (GPU) found in many standard personal computers is a highly parallel math processor capable of nearly 1 TFLOPS peak throughput at a cost similar to a high-end CPU and an excellent FLOPS/watt ratio. High-level linear algebra operations are computationally intense, often requiring O(N3) operations and would seem a natural fit for the processing power of the GPU. Our work is on CULA, a GPU accelerated implementation of linear algebra routines. We present results from factorizations such as LU decomposition, singular value decomposition and QR decomposition along with applications like system solution and least squares. The GPU execution model featured by NVIDIA GPUs based on CUDA demands very strong parallelism, requiring between hundreds and thousands of simultaneous operations to achieve high performance. Some constructs from linear algebra map extremely well to the GPU and others map poorly. CPUs, on the other hand, do well at smaller order parallelism and perform acceptably during low-parallelism code segments. Our work addresses this via hybrid a processing model, in which the CPU and GPU work simultaneously to produce results. In many cases, this is accomplished by allowing each platform to do the work it performs most naturally.

Imaging over long distances is crucial to a number of defense and security applications, such as homeland security and launch tracking. However, the image quality obtained from current long-range optical systems can be severely degraded by the turbulent atmosphere in the path between the region under observation and the imager. While this obscured image information can be recovered using post-processing techniques, the computational complexity of such approaches has prohibited deployment in real-time scenarios. To overcome this limitation, we have coupled a state-of-the-art atmospheric compensation algorithm, the average-bispectrum speckle method, with a powerful FPGA-based embedded processing board. The end result is a light-weight, lower-power image processing system that improves the quality of long-range imagery in real-time, and uses modular video I/O to provide a flexible interface to most common digital and analog video transport methods. By leveraging the custom, reconfigurable nature of the FPGA, a 20x speed increase over a modern desktop PC was achieved in a form-factor that is compact, low-power, and field-deployable.

The modern graphics processing unit (GPU) found in many off-the shelf personal computers is a very high performance computing engine that often goes unutilized. The tremendous computing power coupled with reasonable pricing has made the GPU a topic of interest in recent research. An application for such power would be the solution to large systems of linear equations. Two popular solution domains are direct solution, via the LU decomposition, and iterative solution, via a solver such as the Generalized Method of Residuals (GMRES). Our research focuses on the acceleration of such processes, utilizing the latest in GPU technologies. We show performance that exceeds that of a standard computer by an order of magnitude, thus significantly reducing the run time of the numerous applications that depend on the solution of a set of linear equations.

In this project, we collaborate with researchers in the neuroscience department at the University of Delaware to develop an Field Programmable Gate Array (FPGA)-based embedded computer, inspired by the brains of small vertebrates (fish). The mechanisms of object detection and avoidance in fish have been extensively studied by our Delaware collaborators. The midbrain optic tectum is a biological multimodal navigation controller capable of processing input from all senses that convey spatial information, including vision, audition, touch, and lateral-line (water current sensing in fish). Unfortunately, computational complexity makes these models too slow for use in real-time applications. These simulations are run offline on state-of-the-art desktop computers, presenting a gap between the application and the target platform: a low-power embedded device. EM Photonics has expertise in developing of high-performance computers based on commodity platforms such as graphic cards (GPUs) and FPGAs. FPGAs offer (1) high computational power, low power consumption and small footprint (in line with typical autonomous vehicle constraints), and (2) the ability to implement massively-parallel computational architectures, which can be leveraged to closely emulate biological systems. Combining UD's brain modeling algorithms and the power of FPGAs, this computer enables autonomous navigation in complex environments, and further types of onboard neural processing in future applications.

While imaging over long distances is criti cal to a number of security and defense applications, such as homeland security and launch tracking, current optical systems are limited in resolving power. This is largely a result of the turbulent atmosphere in the path between the region under observation and the imaging system, which can severely degrade captured imagery. There are a variety of post-processing techniques capable of recovering this obscured image information; however, the computational complexity of such approaches has prohibited real-time deployment and hampers the usability of these technologies in many scenarios. To overcome this limitation, we have designed and manufactured an embedded image processing system based on commodity hardware which can compensate for these atmospheric disturbances in real-time. Our system consists of a reformulation of the average bispectrum speckle method coupled with a high-end FPGA processing board, and employs modular I/O capable of interfacing with most common digital and analog video transport methods (composite, component, VGA, DVI, SDI, HD-SDI, etc.). By leveraging the custom, reconfigurable nature of the FPGA, we have achieved performance twenty times faster than a modern desktop PC, in a form-factor that is compact , low-power, and field-deployable. Keywords: bispectral speckle imaging, FPGA , embedded, atmospheric compensati on, real-time image processing

Unmanned Aerial Vehicle (UAV) system integration with naval vessels is currently realized in limited form. The operational envelopes of these vehicles are constricted due to the complexities involved with at-sea flight testing. Furthermore, the unsteady nature of ship airwakes and the use of automated UAV control software necessitates that these tests be extremely conservative in nature. Modeling and simulation are natural alternatives to flight testing; however, a fully-coupled computational fluid dynamics (CFD) solution requires many thousands of CPU hours. We therefore seek to decrease simulation time by accelerating the underlying computations using state-of-the-art, commodity hardware. In this paper we present the progress of our proposed solution, harnessing the computational power of high-end commodity graphics processing units (GPUs) to create an accelerated Euler equations solver on unstructured hexahedral grids.

Photonic crystals[1, 2] have stirred enormous research interest and became a growing enterprise in the last 15 years. Generally, PhCs consist of periodic structures that possess periodicity comparable with the wavelength that the PhCs are designed to modulate. If material and periodic pattern are properly selected, PhCs can be applied to many applications based on their unique properties, including photonic band gaps (PBG)[3], self-collimation[4], super prism[5], etc. Strictly speaking, PhCs need to possess periodicity in three dimensions to maximize their advantageous capabilities. However, many current research is based on scaled two-dimensional PhCs, mainly due to the difficulty of fabrication such three-dimensional PhCs. Many approaches have been explored for the fabrication of 3D photonic crystals, including layer-by-layer surface micromachining[6], glancing angle deposition[7], 3D micro-sculpture method[8], self-assembly[9] and lithographical methods[10-12]. Among them, lithographic methods became increasingly accepted due to low costs and precise control over the photonic crystal structure. There are three mostly developed lithographical methods, namely X-ray lithography[10], holographic lithography[11] and two-photon polymerization[12]. Although significant progress has been made in developing these lithography-based technologies, these approaches still suffer from significant disadvantages. X-ray lithography relies on an expensive radiation source. Holographic lithography lacks the flexibility to create engineered defects, and multi-photon polymerization is not suitable for parallel fabrication. In our previous work, we developed a multi-layer photolithography processes[13, 14] that is based on multiple resist application and enhanced absorption upon exposure. Using a negative lift-off resist (LOR) and 254nm DUV source, we have demonstrated fabrication of 3D arbitrary structures with feature size of several microns. However, severe intermixing problem occurred as we reduced the lattice constant for near-IR applications. In this work, we address this problem by employing SU8. The exposure is vertically confined by using a mismatched 220nm DUV source. Intermixing problem is eliminated due to more densely crosslinked resist molecules. Using this method, we have demonstrated 3D "woodpile" structure with 1.55μm lattice constant and a 2mm-by-2mm pattern area.

Superresolution reconstruction (SR-REC) algorithms combine multiple frames captured using spatially under-sampled imagers to produce a single higher-resolution image. Sub-pixel information is gained from natural motion within the image instead of active pixel scanning (dithering/micro-scanning), eliminating the reliability issues and power consumption associated with moving parts. One of the major computational challenges associated with SR-REC methods is the estimation of the optical flow of the image (i.e., determining the unknown pixel shifts between consecutive frames). A linear least squares approximation is the simplest method for estimating the pixel movements from the captured data, but the size of the problem (directly proportional to the number of pixels in the image) creates a computational bottleneck, which in turn limits the usability of this algorithm in real-time portable systems. We propose the use of a reconfigurable platform to implement these computations in a low power/size environment, suitable for integration into portable millimeter wave imagers.

In this paper, we discuss the real-time compensation of air turbulence in imaging through long atmospheric paths. We propose the use of a reconfigurable hardware platform, specifically field-programmable gate arrays (FPGAs), to reduce costs and development time, as well as increase flexibility and reusability. We present the results of our acceleration efforts to date (40x speedup) and our strategy to achieve a real-time, atmospheric compensation solver for highdefinition video signals.

We present a reconfigurable, compact, low loss, optical switch in silicon. The device utilizes the self-collimation properties of photonic crystal structures and provides a technique for efficiently switching an electromagnetic wave guided through a pre-engineered dispersion based photonic crystal self-guiding structure. The electromagnetic wave can be either in the microwave or optical regime based on the constituent materials and dimensions of the photonic crystal host. We propose that the “loss tangent” of dielectric material in the switching region can be modified by external “commands” to control the direction of propagation of the sel f-collimated signal and hence attain switching, thereby re-directing the light. Based on the geometrical orientation and position of the applied electric field, electromagnetic waves can be completely redirected (switched), or partially routed towards any arbitrary direction on a Manhattan grid or network. We have found that the induced loss does not signi ficantly attenuate the waves switched in any direction. The structure presented can be generalized to an arbitrary N by M interconnected switching network or fabric, where the switching topology can be dynamically modulated by the application of external fields. To attain switching, the free-carrier absorption loss of Si is controlled by carrier injection from forward-biased PN junction. The concept device is designed and analyzed using the FastFDTD

Our group has employed the use of modern graphics processor units (GPUs) for the acceleration of finite-difference based computational electromagnetics (CEM) codes. In particular, we accelerated the well-known Finite-Difference Time-Domain (FDTD) method, which is commonly used for the analysis of electromagnetic phenomena. This algorithm uses difference-based approximations for Maxwell's Equations to simulate the propagation of electromagnetic fields through space and materials. The method is very general and is applicable to a wide array of problems, but runtimes can be very long so acceleration is highly desired. In this paper we present GPU-based accelerated solvers for the FDTD method in both its 2D and 3D embodiments.

There is a growing need for miniature low-cost chemical sensors for use in monitoring environmental conditions. Applications range from environmental pollution monitoring, industrial process control and homeland security threat detection to biomedical diagnostics. Integrated opto-chemical sensors can provide the required functionality by monitoring chemistry induced changes in the refractive, absorptive, or luminescent properties of materials. Mach-Zehnder (MZ) interferometers, using the phase induced from a chemically reactive film, have shown success for such applications but typically are limited to one chemical analysis per sensor. In this paper we present a MZ-like sensor using the dispersion properties of a photonic crystal lattice. Properly engineered dispersion guiding enables the creation of multiple parallel MZ-like sensors monitoring different chemical reactions in a device much smaller than a typical MZ sensor. The phase shift induced in one arm of the photonic crystal structure by the chemical reaction of a special film induces a change in the sensor output. The use of a dispersion guiding photonic crystal structure enables the use of lower refractive index materials because the creation of a bandgap is not necessary. This in turn increases coupling efficiency into the device. Other advantages of this type of structure include the ability to guide both TE and TM modes as well as reduced sensitivity to fabrication tolerances. Two-dimensional FDTD analysis is used to optimize and model the effectiveness of the structure.

The solution of large eigensystems has numerous applications in engineering and science, including circuit simulation, mechanical structure stability, and quantum physics. In particular, many optics and photonics applications, such as the design of photonic crystal slab devices, dispersion engineering, and other iterative-based design techniques, require an eigenvalue solver. Unfortunately, brute force solutions exhibit a computational complexity of O(n 3 ), rendering them entirely impractical for medium to large matrices. Although techniques have been developed to reduce this complexity to O(n 2 ), these algorithms are restricted to special cases such as real, symmetric, or sparse matrices, limiting the applicability of these solutions. Thus, there is a clear need for a high-performance eigenvalue solver for large, non-hermitian matrices. To this end, we are developing a novel, hardware-based platform for the analysis of eigenvalue problems. In this paper, we describe this platform and its application to eigenvalue problems, as well as our progress to date.

Synthetic Aperture Radar (SAR) techniques employ radar waves to generate high-resolution images in all illumination/weather conditions. The onboard implementation of the image reconstruction algorithms allows for the transmission of real-time video feeds, rather than raw radar data, from unmanned aerial vehicles (UAVs), saving significant communication bandwidth. This in turn saves power, enables longer missions, and allows the transmission of more useful information to the ground. For this application, we created a hardware architecture for a portable implementation of the motion compensation algorithms, which are more computationally intensive than the SAR reconstruction itself, and without which the quality of the SAR images is severely degraded, rendering them unusable.

Photonic have crystals shown promise in a variety of applications. Recently, there has been a great interest in designing and building three-dimensional (3D) photonic crystals whose fabrication can be integrated with standard CMOS processes. In this paper, we explore both of these areas in more detail. We first present techniques for modeling 3D photonic crystals and the analysis of practical lattice configurations. We then discuss our process for fabricating such structures built on standard lithography methods. We conclude by showing how these techniques have been used to design and build practical photonic crystal structures capable integration with CMOS circuitry.

In this paper, we discuss the real-time compensation of air turbulence in imaging through long atmospheric paths. We propose the use of a reconfigurable hardware platform, specifically field-programmable gate arrays (FPGAs), to reduce costs and development time, as well as increase flexibility and reusability. We present the results of our acceleration efforts to date (40x speedup) and our strategy to achieve a real-time, atmospheric compensation solver for high-definition video signals.

The performance of modeling and simulation tools is inherently tied to the platform on which they are implemented. In most cases, this platform is a microprocessor, either in a desktop PC, PC cluster, or supercomputer. Microprocessors are used because of their familiarity to developers, not necessarily their applicability to the problems of interest. We have developed the underlying techniques and technologies to produce supercomputer performance from a standard desktop workstation for modeling and simulation applications. This is accomplished through the combined use of graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and standard microprocessors. Each of these platforms has unique strengths and weaknesses but, when used in concert, can rival the computational power of a high-performance computer (HPC). By adding a powerful GPU and our custom designed FPGA card to a commodity desktop PC, we have created simulation tools capable of replacing massive computer clusters with a single workstation. We present this work in its initial embodiment: simulators for electromagnetic wave propagation and interaction. We discuss the trade-offs of each independent technology, GPUs, FPGAs, and microprocessors, and how we efficiently partition algorithms to take advantage of the strengths of each while masking their weaknesses. We conclude by discussing enhancing the computational performance of the underlying desktop supercomputer and extending it to other application areas.

We theoretically investigate the superprism effect in a polymer "woodpile" photonic crystal structure. We find that the common degeneracy problem caused by low index contrast can be avoided at certain frequency range. As a result, polymer materials such like photoresist manifest their potential for high performance superprism. We study the superprism effect for 3 rd and 4 th bands at frequency 0.94c/a using plane wave expansion method. In both cases, the wave propagation direction changes rapidly responding to variation of incident angle. Up to 30 degree of output angle variation for 1 degree of incident angle increment has been observed. We also find an average of 10 degrees shift of output angle for 1% variation of the incident wavelength. We fabricated the proposed "woodpile" structure based on multilayer 3D photolithography. This method, which uses commercially available photoresist, allows batch fabrication of 3D photonic crystals (PhCs), possesses the flexibility to create a variety of different lattice arrangements and the scalability for different operation wavelengths. We describe in this paper how to achieve 3D confined exposure and multiple resist application. We show the fabricated devices for both mid-infrared and telecom applications.