A hardware pipeline for accelerating ray traversal algorithms on streaming processors

Steffen, Michael; Zambreno, Joseph

doi:10.1109/sasp.2010.5521150

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Some of them are implemented for example as an application-specific streaming processor. A few application examples can be found such as streaming processors for fluid dynamic computations based on lattice Boltzmann method [16] and for accelerating ray tracing traversal algorithm in a graphic rendering application [19]. The streaming processor can also be implemented as a reconfigurable streaming processor that can be reconfigured for several scientific computing applications.…”

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Reconfigurable streaming processor core with interconnected floating-point arithmetic units for multicore adaptive signal processing systems

Samman

Surapong

Glesner

2011

6th International Workshop on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC)

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A reconfigurable and programmable streaming processor core complemented with interconnected arithmetic units for the acceleration of floating-point operations is presented in this paper. The streaming processor can be easily reconfigured to perform a complex scientific algorithm or computations by changing the set of instructions in a central control unit. By using floating-point arithmetic unit with pipeline streaming data flow, floating-point operations can be performed in each cycle resulting in a high-performance scientific computations. The streaming processor is dedicated for a high-performance adaptive signal processing applications. For higher performance, reliability and fault-tolerance scientific computations, the streaming processor would be designed as a tile processor in a multicore streaming processor system. I. INTRODUCTIONStreaming processors are commonly used to accelerate the computations of a scientific formulas used in many engineering and information technology application areas. The scientific computations are specifically required to solve some chemical and physical problems, signal and image processing problems and other problems in civil and mechanical engineering. The scientific computations are often very complex and sometimes also require very short time constraints. By using a general-purpose computer system, the problem can be solved well functionally. However, due to the complexity of the scientific computations, high-performance requirements of the computation cannot be meet. Therefore, a specific streaming processor is proposed to solve the hard time constraint problem.Streaming processors have been widely developed for many applications. Some of them are implemented for example as an application-specific streaming processor. A few application examples can be found such as streaming processors for fluid dynamic computations based on lattice Boltzmann method [16] and for accelerating ray tracing traversal algorithm in a graphic rendering application [19]. The streaming processor can also be implemented as a reconfigurable streaming processor that can be reconfigured for several scientific computing applications. The RSVP TM streaming processor presented in [2] can be programmed by describing the shape and the location of vector data streams in memory and describing the computation of the streaming data in a data-flow graphs. However, the RSVP TM streaming processor does not support floating-pointbased arithmetic operations.A few floating-point-based processors that also support streaming computations have been developed so far. Intel Corporation has designed a 6.2-GFlops Floating-Point MultiplyAccumulator (FPMAC) [22]. The FPMAC processors are dedicated for a Teraflops Multicore System, which could run tera floating-point operations per second by interconnecting

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confidence: 99%

Reconfigurable streaming processor core with interconnected floating-point arithmetic units for multicore adaptive signal processing systems

Samman

Surapong

Glesner

2011

6th International Workshop on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC)

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SPECTRON: Streaming processor specific for adaptronic and biomeditronic applications

Samman

Surapong

2012

The 5th 2012 Biomedical Engineering International Conference

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Ahstract-This paper presents a streaming processor specifi cally designed for adaptronic and biomedical engineering applica tions. The main characteristics of the streaming processor are the flexibility to implement floating-poi nt-based scientific computa tions commonly performed in the digital signal processing appli cation. The floating-point operators are connected to dual-port memories through separated 3 operand-buses and 2 resultant buses. Synthesized with 130-nm technology, the Spectron can be clocked at 480 MHz. The processor can perform 4 parallel streaming/pipeline floating-point operations using its FPMAC and CORDIC cores, resulting in a performance of about 4 x 485 = 1.94 GFlops (Giga Floating-point operation per second), which is suitable for high performance image processing in biomedical electronic engineering applications.Streaming processors are commonly used to accelerate the computations of a scientific formulas used in many engineering and information technology application areas. The scientific computations are specifically required to solve some chemical and physical problems, signal and image processing problems and other problems in civil and mechanical engineering. The scientific computations are often very complex and sometimes also require very short computation time (hard real-time constraints). By using a general-purpose computer system, the problem can be solved well functionally. However, due to the complexity of the scientific computations, real-time requirements of the computation cannot be meet. Therefore, a specific streaming processor is proposed to solve the hard real-time problem. So far, streaming processors have been developed by some communities [13] [17], [2], for a specific application and/or for wider range of applications. The RSV P ™ streaming processor for instance [2] can be programmed by describing the shape and the location of vector data streams in memory and describing the computation of the streaming data in a data-flow graphs. However, the RSV P T M streaming processor does not support floating point-based operations.A few floating-point-based processors that also sup port streaming computations have been developed so far. Intel Corporation has designed a 6.2-GFlops Floating Point Multiply-Accumulator (FPMAC) [18]. The FPMAC processors are dedicated for a Teraflops Multicore Sys tem, which could run tera floating-point operations per second by interconnecting 80 FPMAC cores through a 2D mesh 8CElO on-chip network. Another floating-point-based processor that supports streaming computations is SPE 978-1-4673-4892-8/12/$3l . 00

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Toward Real-Time Ray Tracing

Deng

et al. 2017

ACM Comput. Surv.

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Ray tracing has long been considered as the next-generation technology for graphics rendering. Recently, there has been strong momentum to adopt ray tracing--based rendering techniques on consumer-level platforms due to the inability of further enhancing user experience by increasing display resolution. On the other hand, the computing workload of ray tracing is still overwhelming. A 10-fold performance gap has to be narrowed for real-time applications, even on the latest graphics processing units (GPUs). As a result, hardware acceleration techniques are critical to delivering a satisfying level performance while at the same time meeting an acceptable power budget. A large body of research on ray-tracing hardware has been proposed over the past decade. This article is aimed at providing a timely survey on hardware techniques to accelerate the ray-tracing algorithm. First, a quantitative profiling on the ray-tracing workload is presented. We then review hardware techniques for the main functional blocks in a ray-tracing pipeline. On such a basis, the ray-tracing microarchitectures for both ASIC and processors are surveyed by following a systematic taxonomy.

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A hardware pipeline for accelerating ray traversal algorithms on streaming processors

Cited by 3 publications

References 18 publications

Reconfigurable streaming processor core with interconnected floating-point arithmetic units for multicore adaptive signal processing systems

Reconfigurable streaming processor core with interconnected floating-point arithmetic units for multicore adaptive signal processing systems

SPECTRON: Streaming processor specific for adaptronic and biomeditronic applications

Toward Real-Time Ray Tracing

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