Proxy applications (proxy-apps) are basic tools for evaluating the performance of specific workloads on high-performance computing (HPC) systems. Since the development of high-fidelity proxy-apps, which exhibit similar performance characteristics as corresponding production applications, is labor-intensive, synthetic proxy-apps are created as a useful supplement to manually developed proxy-apps. To thoroughly resemble performance characteristics of HPC applications represented by Message Passing Interface (MPI) programs, we propose Siesta, a novel framework to automatically synthesize proxy-apps based on communication-computation traces. Given an MPI program, Siesta synthesizes parameterized code snippets to mimic computation behaviors in different execution periods, and combines the code snippets and MPI function records into an event trace. It then extracts program behavior patterns from the trace as grammars and finally transforms the grammars into a synthetic proxy-app. We evaluate the proposed methods on representative MPI programs with various environments. The results show that our synthetic proxy-apps can precisely approximate the performance characteristics of MPI programs,

The Transformer model, which has significantly advanced natural language processing and computer vision, overcomes the limitations of recurrent neural networks and convolutional neural networks. However, it faces challenges with computational efficiency and memory management due to complex computations and variable-length inputs. Despite research efforts, these issues persist. This paper presents a novel optimization of the Transformer model, following an indepth analysis of its computational graph structure. Firstly, we utilize Deep Computing Unit (DCU) as our hardware platform. Secondly, we optimize element-wise and reduction operators through operator fusion and rewriting. Thirdly, we develop a fine-grained memory management algorithm using a greedy strategy. As a result, the training speed of the Transformer model increases by 1.2x - 1.4x without compromising accuracy.

We introduce a highly robust GAN-based framework for digitizing a normalized 3D avatar of a person from a single unconstrained photo. While the input image can be of a smiling person or taken in extreme lighting conditions, our method can reliably produce a high-quality textured model of a person’s face in neutral expression and skin textures under diffuse lighting condition. Cutting-edge 3D face reconstruction methods use non-linear morphable face models combined with GAN-based decoders to capture the likeness and details of a person but fail to produce neutral head models with unshaded albedo textures which is critical for creating relightable and animation-friendly avatars for integration in virtual environments. The key challenges for existing methods to work is the lack of training and ground truth data containing normalized 3D faces. We propose a two-stage approach to address this problem. First, we adopt a highly robust normalized 3D face generator by embedding a non-linear morphable face model into a StyleGAN2 network. This allows us to generate detailed but normalized facial assets. This inference is then followed by a perceptual refinement step that uses the generated assets as regularization to cope with the limited available training samples of normalized faces. We further introduce a Normalized Face Dataset, which consists of a combination photogrammetry scans, carefully selected photographs, and generated fake people with neutral expressions in diffuse lighting conditions. While our prepared dataset contains two orders of magnitude less subjects than cutting edge GAN-based 3D facial reconstruction methods, we show that it is possible to produce high-quality normalized face models for very challenging unconstrained input images, and demonstrate superior performance to the current state-of-the-art.

Wearable VR/AR devices provide users with fully immersive experience in a virtual environment, enabling possibilities to reshape the forms of entertainment and telepresence. While the body language is a crucial element in effective communication, wearing a head-mounted display (HMD) could severely hinder the eye contact and block facial expressions. We present a novel headset removal technique that enables high-quality occlusion-free communication in virtual environment. In particular, our solution synthesizes photoreal faces in the occluded region with faithful reconstruction of facial expressions and eye movements. Towards this goal, we develop a novel capture setup that consists of two near-infrared (NIR) cameras inside the HMD for eye capturing and one external RGB camera for recording visible face regions. To enable realistic face synthesis with consistent illuminations, we propose a data-driven approach to fuse the narrow-field-of-view NIR images with the RGB image captured from the external camera. In addition, to generate pho-torealistic eyes, a dedicated algorithm is proposed to colorize the NIR eye images and further rectify the color distortion caused by the non-linear mapping of IR light sensitivity. Experimental results demonstrate that our framework is capable to synthesize high-fidelity unoccluded facial images with accurate tracking of head motion, facial expression and eye movement.

Service-oriented architecture (SOA) and model-driven architecture (MDA) are the hottest topics of discussion currently with regard to enterprise architecture. This paper presents an approach for transforming computer independent model (CIM) to platform independent model (PIM) separating implementation technologies and platforms from the business logic in an easy and uniform way. The approach mainly includes the following two parts. The first part gives a service-oriented way to model the requirement. The requirement model addresses software as a service model for forward looking enterprise; the second part gives a model refinement mechanism and a set of refinement rules. The refinement mechanism and rules can transform the requirement model to a set of loosely coupled services and can match these services to their suitable components and interfaces. In a word, this paper provides a uniform way to transform business requirements to their service implementation, and this transforming process can be clearly traced and analyzed.