Recent advances in computing have led to an explosion in the amount of data being generated. Processing the ever-growing data in a timely manner has made throughput computing an important aspect for emerging applications. Our analysis of a set of important throughput computing kernels shows that there is an ample amount of parallelism in these kernels which makes them suitable for today's multi-core CPUs and GPUs. In the past few years there have been many studies claiming GPUs deliver substantial speedups (between 10X and 1000X) over multi-core CPUs on these kernels. To understand where such large performance difference comes from, we perform a rigorous performance analysis and find that after applying optimizations appropriate for both CPUs and GPUs the performance gap between an Nvidia GTX280 processor and the Intel Core i7-960 processor narrows to only 2.5x on average. In this paper, we discuss optimization techniques for both CPU and GPU, analyze what architecture features contributed to performance differences between the two architectures, and recommend a set of architectural features which provide significant improvement in architectural efficiency for throughput kernels.

/pdf/debunking-the-100x-gpu-vs-cpu-myth-an-evaluation-of-1m7y4u1n7m.pdf

Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

This paper presents a many-core visual computing architecture code named Larrabee, a new software rendering pipeline, a manycore programming model, and performance analysis for several applications. Larrabee uses multiple in-order x86 CPU cores that are augmented by a wide vector processor unit, as well as some fixed function logic blocks. This provides dramatically higher performance per watt and per unit of area than out-of-order CPUs on highly parallel workloads. It also greatly increases the flexibility and programmability of the architecture as compared to standard GPUs. A coherent on-die 2nd level cache allows efficient inter-processor communication and high-bandwidth local data access by CPU cores. Task scheduling is performed entirely with software in Larrabee, rather than in fixed function logic. The customizable software graphics rendering pipeline for this architecture uses binning in order to reduce required memory bandwidth, minimize lock contention, and increase opportunities for parallelism relative to standard GPUs. The Larrabee native programming model supports a variety of highly parallel applications that use irregular data structures. Performance analysis on those applications demonstrates Larrabee's potential for a broad range of parallel computation.

/pdf/larrabee-a-many-core-x86-architecture-for-visual-computing-58j94b6w2n.pdf

Larrabee: a many-core x86 architecture for visual computing

Moving from a set of independent virtual worlds to an integrated network of 3D virtual worlds or Metaverse rests on progress in four areas: immersive realism, ubiquity of access and identity, interoperability, and scalability. For each area, the current status and needed developments in order to achieve a functional Metaverse are described. Factors that support the formation of a viable Metaverse, such as institutional and popular interest and ongoing improvements in hardware performance, and factors that constrain the achievement of this goal, including limits in computational methods and unrealized collaboration among virtual world stakeholders and developers, are also considered.

/pdf/3d-virtual-worlds-and-the-metaverse-current-status-and-2tcyj4edj0.pdf

3D Virtual worlds and the metaverse: Current status and future possibilities

As one of the most promising energy-efficient computing paradigms, approximate computing has gained a lot of research attention in the past few years. This paper presents a survey of state-of-the-art work in all aspects of approximate computing and highlights future research challenges in this field.

Approximate Computing: A Survey

Bioinspired hardware holds the promise of low-energy, intelligent, and highly adaptable computing systems. Applications span from automatic classification for big data management, through unmanned vehicle control, to control for biomedical prosthesis. However, one of the major challenges of fabricating bioinspired hardware is building ultrahigh-density networks out of complex processing units interlinked by tunable connections. Nanometer-scale devices exploiting spin electronics (or spintronics) can be a key technology in this context. In particular, magnetic tunnel junctions (MTJs) are well suited for this purpose because of their multiple tunable functionalities. One such functionality, nonvolatile memory, can provide massive embedded memory in unconventional circuits, thus escaping the von-Neumann bottleneck arising when memory and processors are located separately. Other features of spintronic devices that could be beneficial for bioinspired computing include tunable fast nonlinear dynamics, controlled stochasticity, and the ability of single devices to change functions in different operating conditions. Large networks of interacting spintronic nanodevices can have their interactions tuned to induce complex dynamics such as synchronization, chaos, soliton diffusion, phase transitions, criticality, and convergence to multiple metastable states. A number of groups have recently proposed bioinspired architectures that include one or several types of spintronic nanodevices. In this paper, we show how spintronics can be used for bioinspired computing. We review the different approaches that have been proposed, the recent advances in this direction, and the challenges toward fully integrated spintronics complementary metal–oxide–semiconductor (CMOS) bioinspired hardware.

/pdf/spintronic-nanodevices-for-bioinspired-computing-1marhe96x1.pdf

Spintronic Nanodevices for Bioinspired Computing

Unlike online games, metaverses present a single seamless, persistent world where users can transparently roam around without predefined objectives. An analysis of Second Life illustrates the demands such applications place on clients, servers, and the network and suggests possible optimizations.

Second Life and the New Generation of Virtual Worlds

This paper examines the growing need for a general-purpose ldquoanalytics enginerdquo that can enable next-generation processing platforms to effectively model events, objects, and concepts based on end-user input, and accessible datasets, along with an ability to iteratively refine the model in real-time. We find such processing needs at the heart of many emerging applications and services. This processing is further decomposed in terms of an integration of three fundamental compute capabilities-recognition, mining, and synthesis (RMS). The set of RMS workloads is examined next in terms of usage, mathematical models, numerical algorithms, and underlying data structures. Our analysis suggests a workload convergence that is analyzed next for its platform implications. In summary, a diverse set of emerging RMS applications from market segments like graphics, gaming, media-mining, unstructured information management, financial analytics, and interactive virtual communities presents a relatively focused, highly overlapping set of common platform challenges. A general-purpose processing platform designed to address these challenges has the potential for significantly enhancing users' experience and programmer productivity.

Convergence of Recognition, Mining, and Synthesis Workloads and Its Implications

Medical volumetric imaging requires high fidelity, high performance rendering algorithms. We motivate and analyze new volumetric rendering algorithms that are suited to modern parallel processing architectures. First, we describe the three major categories of volume rendering algorithms and confirm through an imaging scientist-guided evaluation that ray-casting is the most acceptable. We describe a thread- and data-parallel implementation of ray-casting that makes it amenable to key architectural trends of three modern commodity parallel architectures: multi-core, GPU, and an upcoming many-core Intelreg architecture code-named Larrabee. We achieve more than an order of magnitude performance improvement on a number of large 3D medical datasets. We further describe a data compression scheme that significantly reduces data-transfer overhead. This allows our approach to scale well to large numbers of Larrabee cores.

/pdf/mapping-high-fidelity-volume-rendering-for-medical-imaging-2thk1hss8x.pdf

Mapping High-Fidelity Volume Rendering for Medical Imaging to CPU, GPU and Many-Core Architectures

In some embodiments, the invention involves utilizing a tree merge sort in a platform to minimize cache reads/writes when sorting large amounts of data. An embodiment uses blocks of pre-sorted data residing in “leaf nodes” residing in memory storage. A pre-sorted block of data from each leaf node is read from memory and stored in faster cache memory. A tree merge sort is performed on the nodes that are cache resident until a block of data migrates to a root node. Sorted blocks reaching the root node are written to memory storage in an output list until all pre-sorted data blocks have been moved to cache and merged upward to the root. The completed output list in memory storage is a list of the fully sorted data. Other embodiments are described and claimed.

System and method for memory bandwidth friendly sorting on multi-core architectures

Methods, apparatuses and systems to decrease the energy consumption of a memory chip while increasing its effect bandwidth during the execution of any workload. Methods, apparatuses and systems may allow a memory chip utilize a plurality of virtual row buffers to respond to requests for data included in a memory array block. Methods, apparatuses and systems may further eliminate or reduce the cost associated with transferring unnecessary data from a memory array block to row buffers by altering the data transfer size between a memory array block and a row buffer.

Anthony-Trung D. Nguyen

Papers

Second Life and the New Generation of Virtual Worlds

Convergence of Recognition, Mining, and Synthesis Workloads and Its Implications

Mapping High-Fidelity Volume Rendering for Medical Imaging to CPU, GPU and Many-Core Architectures

System and method for memory bandwidth friendly sorting on multi-core architectures

Virtual row buffers for use with random access memory