Rodinia Fpga

DAS Day, @ TUDelft, 13/02/2013 16 Pro's and Con's Rodinia Graph 64K 393,216 64K Rodinia benchmark. Rodinia Parboil SHOC Valar SPEC-ACCEL Mainly HPC workloads No OpenCL and CUDA Partially Mainly GPGPU SHOC-MIC Mainly HPC workloads Yes OpenMP Partially Xeon PhiTM PhiBench Data analytics workloads Yes OpenMP and Cilk Plus Yes Xeon PhiTM Though SHOC [7] provides an optimized version [28] on Xeon PhiTM, but it mainly covers HPC workloads. We have experience with following Industries and Sciences. Because porting such applications is a large effort, we’ve focused on the 10 smallest benchmarks. Description B1. to be executed on Intel (formerly Altera) FPGA platforms. Lastly, for the GPUs, we used the Parboil, Rodinia, and SHOC benchmarking suites. Built using 3 FPGA chips. independent emulator (19), an emulator for an Altera FPGA (20), and the associated FPGA device (21). In [5], an FPGA-GPU-CPU architecture is used where steps of the algorithm requiring deep pipelining and small buffers are mapped to FPGA, steps requiring massively parallel operations with large buffers leverage GPU, and the CPU is used for coordination, low throughput and branching dominated tasks. CHO - CHO is a suite of benchmark applications for OpenCL FPGA platforms. -Runs OpenCL programs and prototyped on FPGA -Similar design to industry state-of-art -Similar performance to industry state-of-art* -Flexible and Extendable •MIOAW's hardware design is Open Source •Contributes to changing hardware landscape * Frequency, Physical Design, Area goals relaxed 2. I characterized the Rodinia and Parboil benchmarks based on various metrics as kernel runtime, function runtime, sensitivity to bandwidth and latency, on-chip and off-chip memory usage and found the hot basic blocks in CFG as well as the common patterns in DFG to be moved to a GPU accelerator so as to optimize the kernel(s) runtime and reduce the bandwidth usage. Intel has created Intel® FPGA SDK for OpenCL™ technology 1, which provides an alternative to HDL programming. See the complete profile on LinkedIn and discover Ramkumar's connections and jobs at similar companies. Compilers Creating Custom Processors (CCCP) Research Group. See README_original for the original description, or visit here for more details. If you are interested in something that isn't listed here please don't hesitate to contact us. 4475223948738: 1542: mug: 12. 2x (using 8 dual-issue cores) and on one case, super-linear improvement of 8. View Ramkumar Shankar's profile on LinkedIn, the world's largest professional community. a subset of the Rodinia benchmark suite for two generations of Intel FPGAs, and then optimize each benchmark based on the specific architectural characteristics of these FPGAs. 9 billion years ago and broke up 750-633 million years ago. Malware detection programs act as a function whose domain is the inspected code, and the image is a set of two outputs, i. Turning to Heterogeneous Chips. Abhay has 5 jobs listed on their profile. See the complete profile on LinkedIn and discover Ramkumar’s connections and jobs at similar companies. Abstract: This paper aims to better understand the performance differences between FPGAs and GPUs. Across 12 OpenCL benchmarks results demonstrate near-linear wall-clock performance. For GPU implementations, we use the NVIDIA Tesla* P100 PCIe 12GB GPU with CUDA* 8. Describe any specific hardware and its features strictly required to evaluate your artifact (vendor, CPU/GPU/FPGA, number of processors/cores, interconnect, memory, hardware counters, etc). Rodinia: A Benchmark Suite for Heterogeneous Computing 31 OpenCL Host Code The code is from NVIDIA Corporation, NVIDIA OpenCL JumpStart Guide, April 2009 Rodinia: A Benchmark Suite for Heterogeneous Computing 32 Questions and Discussions • What is the best way to deal with the portability and legacy code issues?. Free LogiCORE™ IP design enabling the use of multi-gigabit transceivers for Xilinx FPGA. 6622452297866: 1201: mum: 12. 6622452297866: 1201: mum: 12. View Aishwarya Aniruddha Kulkarni's profile on LinkedIn, the world's largest professional community. FPGA-based Soccer Video Game Development October 2011 - December 2011. Kuan-Hsun Chen, Niklas Ueter, Georg von der Brüggen and Jian-Jia Chen. marks in the Rodinia Benchmark Suite [4]. Aishwarya Aniruddha has 9 jobs listed on their profile. If you are interested in something that isn't listed here please don't hesitate to contact us. When I started penning the title to this column, I began with "Have you got the globes?" and then the British comedy duo Morecambe and Wise popped into my mind, and I could imagine Ernie Wise asking this question and I could hear Eric Morecambe respond "No, I always walk this way!". Exploiting DVFS for GPU Energy Management Testing was made using a set of benchmarks from the Rodinia suite and FPGA Field-Programmable Gate Array. Intel OpenCL for FPGA (reps for Deep-pipeline designs) We evaluated using Rodinia Benchmark Suite on Stratix V FPGA. We use the polyhedral compiler to generate highly optimized OpenCL code for a set of standard benchmark suites (Rodinia and SHOC), image processing kernels, and for two DSL compilers: a linear algebra (BLAS) DSL compiler and a DSL for signal processing radar applications (SpearDE). FCUDA-SoC: Platform Integration for Field-Programmable SoC with the CUDA-to-FPGA Compiler Tan Nguyen 1, Swathi Gurumani1, Kyle Rupnow , Deming Chen2 1 Advanced Digital Sciences Center, Singapore {tan. Authors in [15] explore the performance of OpenCL by porting the Rodinia benchmark. OVERALL FPGA DESIGN STRATEGIES FOR RODINIA We take the original code from Rodinia and make it HLS C synthesizable on the FPGA, which serves as the FPGA baseline. 2217954719722: 1141: 2002: 9. optimizations developed for the OpenACC to FPGA framework. This is the best published result to our best knowledge. Lowell, USA. Intel OpenCL for FPGA (reps for Deep-pipeline designs) We evaluated using Rodinia Benchmark Suite on Stratix V FPGA. That also supports non-CUDA accelerators. • Expand I/O with the FPGA Mezzanine Card (FMC) interface Rodinia benchmarks executing on both the GPU and the CPU (on all 4 A57, one is observed). The FPGA implementation of the dot-product relied on the multiply-accumulate pipeline built into the DSP18 blocks in the StratixIV FPGA. 08x, and for the Rodinia benchmarks we achieve a mean speedup of 1. Scogland, and W. However, the separate host and device compilation approach advocated by OpenCL hides compiler optimization opportunities that can dramatically improve FPGA performance. A case study of multiprogram workloads where we devise a scheduling policy, relying on thread migrations, to minimize a workload's average turnaround time. Using the OpenCL API, developers can launch compute kernels written using a limited subset of the C programming language on a GPU. sg 2 Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, USA [email protected] I characterized the Rodinia and Parboil benchmarks based on various metrics as kernel runtime, function runtime, sensitivity to bandwidth and latency, on-chip and off-chip memory usage and found the hot basic blocks in CFG as well as the common patterns in DFG to be moved to a GPU accelerator so as to optimize the kernel(s) runtime and reduce. You could be running against a FPGA, a DSP, or even a manycore CPU like Knight's Landing. For the SoC-FPGA implementation the tool-chain output results in two binary files, IRAM and DRAM for the kernel image and initialised data section. It is connected the to host via PCIe. When I started penning the title to this column, I began with "Have you got the globes?" and then the British comedy duo Morecambe and Wise popped into my mind, and I could imagine Ernie Wise asking this question and I could hear Eric Morecambe respond "No, I always walk this way!". Julia is a high-level programming language for mathematical computing that is as easy to use as Python, but as fast as C. See the complete profile on LinkedIn and discover Ramkumar’s connections and jobs at similar companies. Davesh has 4 jobs listed on their profile. 1, and the Intel FPGA OpenCL High-Performance Computing Platform Examples. Designing On-chip Memory Systems for Throughput Architectures. This is the best published result to our best knowledge. The applications were chosen based on several domains and include a variety of computational methods. School of Computing Science. Order Now! RF/IF and RFID ship same day. Accelerators are increasingly popular… Good for performance, energy-efficiency, programmability, exciting new applications…. We have designed a simple generic shared memory architecture and synthesized it to 2, 4, 8, 16, … , 1024 - cores for FPGA virtex-7. With various famous reference networks. The IOComplexity associate team is an active collaboration between the research group of P. al, IISWC’09] OpenCL GPU benchmarking 21 PolyBench [Pouchet, 2010] C Polyhedral analysis 30 MachSuite [Reagen et. Acceleration of Frequent Itemset Mining on FPGA using SDAccel and Vivado HLS. nguyen, swathi. We will be adding more applications with time. View Abhay Tambe's profile on LinkedIn, the world's largest professional community. Michael Pellauer , Michael Adler , Michel Kinsy , Angshuman Parashar , Joel Emer, HAsim: FPGA-based high-detail multicore simulation using time-division multiplexing, Proceedings of the 2011 IEEE 17th International Symposium on High Performance Computer Architecture, p. We have experience with following Industries and Sciences. The benchmarks are executed on 40 LE1 configurations with 10 implemented on an SoC-FPGA and the remaining on a cycle-accurate simulator. Further, because of extremely long FPGA synthesis times, the overhead of recompiling the host code for each compilation of FPGA kernel code is relatively inexpensive. The synthesis results show that, for the FPGA, there is hardly any effect of the communication network on the execution time and that such architectures can scale-up without any quadratic penalty. We provide a simple five-step strategy for FPGA accelerator designs that can be easily understood and mastered by software programmers, and present. Across 12 OpenCL benchmarks results demonstrate near-linear wall-clock performance. Sehen Sie sich auf LinkedIn das vollständige Profil an. frequency of operation) and more recently using FPGA taking advantage of the possibility to describe multiple types of hardware on the same device, reconfiguring it as our system is evolving with newer components. Across 12 OpenCL benchmarks results demonstrate near-linear wall-clock performance. Oren_Segal/[email protected] student. View Ramkumar Shankar’s profile on LinkedIn, the world's largest professional community. Janus is tailored to, but not limited to, the needs of a class of hard scientific applications characterized by regular code structure, unconventional data-manipulation requirements, and a few. These evaluations were used to generate the figures in the bottom-right panel of the poster. The applications were chosen based on several domains and include a variety of computational methods. The benchmarks are executed on 40 LE1 configurations with 10 implemented on an SoC-FPGA and the remaining on a cycle-accurate simulator. The platforms used were a Virtex-7 FPGA and Tesla K40c GPU. 406-417, February 12-16, 2011. For GPU implementations, we use the NVIDIA Tesla* P100 PCIe 12GB GPU with CUDA* 8. Lowell, USA. Feng, “Architecture-Aware Mapping and Optimizations on a 1600-Core GPU,” 17th IEEE Int’l Conf. - Analysis of the Rodinia benchmark for FPGA implementation potential (complete implementations and CPU-to-FPGA offloading) - Architectural exploration and development of FPGA IP cores of chosen. Abstract: We evaluate the power and performance of the Rodinia benchmark suite using the Altera SDK for OpenCL targeting a Stratix V FPGA against a modern CPU and GPU. 1 release) in our FPGA and GPU comparison. The FPGA implementation of the dot-product relied on the multiply-accumulate pipeline built into the DSP18 blocks in the StratixIV FPGA. They ported 15 of its kernels using Vivado HLS for the FPGA and OpenCL for host programs. Look for a new release. However, unpredictablility of automated provers in handling quantified formulas presents a major hurdle to usability of these tools. The technology and related tools belong to a class of techniques called high-level synthesis (HLS) that enable designs to be expressed with higher levels of abstraction. We study multiple OpenCL kernels per benchmark, ranging from direct ports of the original GPU implementations to loop-pipelined. Rodinia Parboil SHOC Valar SPEC-ACCEL Mainly HPC workloads No OpenCL and CUDA Partially Mainly GPGPU SHOC-MIC Mainly HPC workloads Yes OpenMP Partially Xeon PhiTM PhiBench Data analytics workloads Yes OpenMP and Cilk Plus Yes Xeon PhiTM Though SHOC [7] provides an optimized version [28] on Xeon PhiTM, but it mainly covers HPC workloads. Xilinx Zynq UltraScale+ MPSoC 3 4 CPUs ARM Cortex-A53 GPU ARM Mali 400 2x CPUs ARM Cortex-R5 FPGA 4. optimizations developed for the OpenACC to FPGA framework. We present Run-Length Base-Delta (RLBD). What is OpenACC? OpenACC is a user-driven directive-based performance-portable parallel programming model designed for scientists and engineers interested in porting their codes to a wide-variety of heterogeneous HPC hardware platforms and architectures with significantly less programming effort than required with a low-level model. Their evaluation. al, JIP'08] C Synthesizability check 12 Rodinia [Che et. For both cost and performance reasons, computing systems tightly couple parts realized in hardware with parts realized in software. For the SoC-FPGA implementation the tool-chain output results in two binary files, IRAM and DRAM for the kernel image and initialised data section. Thus, while some of the applications in these benchmarks suites are applicable to studying the OpenCL to FPGA design flow, they require modifications to be useful. View Davesh Shingari's profile on LinkedIn, the world's largest professional community. Describe any specific hardware and its features strictly required to evaluate your artifact (vendor, CPU/GPU/FPGA, number of processors/cores, interconnect, memory, hardware counters, etc). 4475223948738: 1542: mug: 12. benchmarks drawn from the AMD SDK and the Rodinia suites. Sathre ABSTRACT The use of hardware accelerators in high-performance computing has grown increasingly prevalent, particularly due to the growth of graphics processing units (GPUs) as general-purpose (GPGPU) accelerators. For GPU implementations, we use the NVIDIA Tesla* P100 PCIe 12GB GPU with CUDA* 8. Rodinia: A Benchmark Suite for Heterogeneous Computing, Shuai Che (University of Virginia), Michael Boyer (University of Virginia), Jiayuan Meng (University of Virginia), David Tarjan (University of Virginia), Sang-Ha Lee (University of Virginia), Jeremy Sheaffer (University of Virginia), Kevin Skadron (University of Virginia). Specialized and GPU Benchmark Suites • The suite consists of four applications and five kernels. Our transformations are integrated with the Intel FPGA SDK for OpenCL and are evaluated on a subset of the Rodinia benchmark suite using an Altera Stratix V FPGA. c 2015 Hee-Seok Kim. See the complete profile on LinkedIn and discover Ramkumar's connections and jobs at similar companies. Oren Segal, Nasibeh Nasiri, Martin Margala. A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads S Che, JW Sheaffer, M Boyer, LG Szafaryn, L Wang, K Skadron IEEE International Symposium on Workload Characterization (IISWC'10), 1-11 , 2010. , allocating all resources and transitioning to a low-power idle state when a task completes. This represents the hardware that was available to us at Imperial College, and spans a wide range of OpenCL-capable devices. I characterized the Rodinia and Parboil benchmarks based on various metrics as kernel runtime, function runtime, sensitivity to bandwidth and latency, on-chip and off-chip memory usage and found the hot basic blocks in CFG as well as the common patterns in DFG to be moved to a GPU accelerator so as to optimize the kernel(s) runtime and reduce the bandwidth usage. Axel uses NNUS (non-uniform node, uniform system) architecture, where heterogeneous PUs (viz. 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA'18) [雑誌論文] Optimizing the Rodinia Benchmark for FPGAs 2015. They ported 15 of its kernels using Vivado HLS for the FPGA and OpenCL for host programs. Department of Electrical and Computer Engineering. benchmarks drawn from the AMD SDK and the Rodinia suites. 000+ aktuelle jobopslag i Danmark og andre lande. ;top titles;ISBN;hyperlinks;last name of 1st author;authors without affiliation;title;subtitle;series;ed. a subset of the Rodinia benchmark suite for two generations of Intel FPGAs, and then optimize each benchmark based on the specific architectural characteristics of these FPGAs. Kuan-Hsun Chen, Niklas Ueter, Georg von der Brüggen and Jian-Jia Chen. High Level Programming of FPGAs for HPC and Data Centric Applications. NAS, PARBOIL, Rodinia and others. This paper presents and characterizes Rodinia, a benchmark suite for heterogeneous computing. That also supports non-CUDA accelerators. 0 on the Stratix V board. Xilinx Zynq UltraScale+ MPSoC 3 4 CPUs ARM Cortex-A53 GPU ARM Mali 400 2x CPUs ARM Cortex-R5 FPGA 4. 2019-10:00: "Overview of FPGA-based Deep Neural Network techniques". Rodinia Solutions has a wide variety of experience and expertise. Getting you to run your software faster is our primary goal - if your industry or science is not listed, contact us to learn how we can also help you. Davesh has 4 jobs listed on their profile. Sculptor:Flexible Approximation with Selective Dynamic Loop Perforation. In an effort to extend our knowledge to FPGA architectures, we made the necessary changes to Rodinia so it would be able to run on FPGAs. Read more>. ECE Distinguished Seminar. View Ramkumar Shankar’s profile on LinkedIn, the world's largest professional community. The FPGA implementation of the dot-product relied on the multiply-accumulate pipeline built into the DSP18 blocks in the StratixIV FPGA. View Alexey Kravets’ profile on LinkedIn, the world's largest professional community. FPGA-based Soccer Video Game Development October 2011 - December 2011. 30x over 8 of 19 kernels that were determined safe to coarsen. compute-intensive benchmarks in OpenCL targeting FPGA, GPU, and CPU. I have more than 20 US patent applications which are approved and being filed. The choice of applications is inspired by Berkeley’s dwarf taxonomy. Optimized HLS implementation on real FPGA devices 3 Related Work Benchmark Suite Language Primary Focus #Benchmarks CHStone [Hara et. The utility of the bottleneck analysis is demonstrated in two contexts: 1) Coupling the new bottleneck-driven optimization strategy with the OpenTuner auto-tuner: experimental results on all kernels from the Rodinia suite and GPU tensor contraction kernels from the NWChem computational chemistry suite demonstrate effectiveness. Although this study includes some vision kernels such as: GICOV, Dilate, SRAD and MGVF, it was not mainly focused on benchmarking vision algorithms; it included other kernels for. J Sign Process Syst DOI 10. Four DSP18s were required for each 8 × 8 dot-product block and, because very few of the FPGA’s other resources were required, this was the limiting factor in determining the number of parallel units. Caffeine achieves a peak performance of 365 GOPS on Xilinx KU060 FPGA and 636 GOPS on Virtex7 690t FPGA. For example, Rodinia [5], originally. Authors in [15] explore the performance of OpenCL by porting the Rodinia benchmark. OS-FPGA, OS-VP interface, lightweight RTL Energy-efficient programming and energy optimisation of mobile workloads at/across levels. We evaluate the power and performance of the Rodinia benchmark suite using the Altera SDK for OpenCL targeting a Stratix V FPGA against a modern CPU and GPU. Our transformations are integrated with the Intel FPGA SDK for OpenCL and are evaluated on a subset of the Rodinia benchmark suite using an Altera Stratix V FPGA. See the complete profile on LinkedIn and discover Aishwarya Aniruddha's connections and jobs at similar companies. Research Assistant, Department of Computer Sciences University of Wisconsin-Madison gennaio 2013 – marzo 2017 4 anni 3 mesi. [email protected] 所属 (過去の研究課題情報に基づく):国立研究開発法人理化学研究所,計算科学研究機構,チームリーダー, 研究分野:計算機システム・ネットワーク,理工系,高性能計算, キーワード:ハイパフォーマンスコンピューティング,高性能計算,gpu計算,可搬性,グリッド,耐故障性技術,ハイパフォーマンス. 2 Virtex-4 LX200, 1 Virtex-4 FX100 Slide 1 Last modified by:. Speedup of 73. 8x (using 2 dual-issue cores), up to 5. The synthesis results show that, for the FPGA, there is hardly any effect of the communication network on the execution time and that such architectures can scale-up without any quadratic penalty. Oren_Segal/[email protected] student. It is connected the to host via PCIe. Making the most out of Heterogeneous Chips with CPU, GPU and FPGA. What can we do if they're. Xilinx Zynq UltraScale+ MPSoC 3 4 CPUs ARM Cortex-A53 GPU ARM Mali 400 2x CPUs ARM Cortex-R5 FPGA 4. Thus, while some of the applications in these benchmarks suites are applicable to studying the OpenCL to FPGA design flow, they require modifications to be useful. OpenCL model on FPGA architecture. Sehen Sie sich das Profil von Sopan Patra auf LinkedIn an, dem weltweit größten beruflichen Netzwerk. 71 over Intel's state-of-the-art implementations on Parboil and Rodinia ii. , Muirhead, Brian, Wright, Graeme, and Bird, Michael A. To help architects study emerging platforms such as GPUs (Graphics Processing Units), Rodinia includes applications and kernels which target multi-core CPU and GPU platforms. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. Their evaluation. The benchmarks are executed on 40 LE1 configurations with 10 implemented on an SoC-FPGA and the remaining on a cycle-accurate simulator. The CPU and GPU suites tested mathematical algorithms, high performance simulation, and common computational necessities such as compression and sorting. For example, Rodinia [5], originally. The technology and related tools belong to a class of techniques called high-level synthesis (HLS) that enable designs to be expressed with higher levels of abstraction. This represents the hardware that was available to us at Imperial College, and spans a wide range of OpenCL-capable devices. School of Computing Science. Terasic is the world’s leading designer and vendor. J Sign Process Syst DOI 10. Rodinia currently provides single-threaded C, OpenMP, and CUDA implementations of a diverse set of applications to use as benchmarks in architecture, compiler, and programming-language research; we also have a couple of FPGA implementations and are working to expand these. The IOComplexity associate team is an active collaboration between the research group of P. To obtain a hight throughput I want to read an write buffers simultaneously but unfortunately openCL executes the commands consecutive. • Not portable across more than one type of platform except for OpenCL • Most models are heavy-weight for embedded processors of limited resources • Most models require support from OS and compilers. ;top titles;ISBN;hyperlinks;last name of 1st author;authors without affiliation;title;subtitle;series;ed. Accelerators are increasingly popular… Good for performance, energy-efficiency, programmability, exciting new applications…. It's definitely tied less tightly to the hardware, and that's really my main objection to it too. We find results that support some distinct mappings between the architecture and performance per watt. We provide a simple five-step strategy for FPGA accelerator designs that can be easily understood and mastered by software programmers, and present. Agegnehu, Getachew, Bass, Adrian M. Compilers Creating Custom Processors (CCCP) Research Group. See README_original for the original description, or visit here for more details. Oren_Segal/[email protected] student. Imaña Pascual, José Luis (2018) Efficient FPGA implementation of binary field multipliers based on irreducible trinomials. Abhay has 5 jobs listed on their profile. We study multiple OpenCL kernels per benchmark, ranging from direct ports of the original GPU implementations to loop-pipelined. Several of our benchmarks are found in these existing benchmark suites. FPGA-based Soccer Video Game Development October 2011 - December 2011. Look for a new release. Software dependencies Describe any specific OS and software packages required to evaluate your artifact. Making the most out of Heterogeneous Chips with CPU, GPU and FPGA Rafael Asenjo Dept. Sculptor:Flexible Approximation with Selective Dynamic Loop Perforation. Professor Dept. compute-intensive benchmarks in OpenCL targeting FPGA, GPU, and CPU. Experiments demonstrate that. , Muirhead, Brian, Wright, Graeme, and Bird, Michael A. Our transformations are integrated with the Intel FPGA SDK for OpenCL and are evaluated on a subset of the Rodinia benchmark suite using an Altera Stratix V FPGA. To help architects study emerging platforms such as GPUs (Graphics Processing Units), Rodinia includes applications and kernels which target multi-core CPU and GPU platforms. These evaluations were used to generate the figures in the bottom-right panel of the poster. I am running rodinia applications using the Intel SDK HLS 16. Getting you to run your software faster is our primary goal - if your industry or science is not listed, contact us to learn how we can also help you. Axel uses MapReduce framework where GPU and FPGA work on the Map part in parallel and CPU works on the Reduce part. GPUs and FPGAs. Then we propose an analytical model to compare their performance. In Proceedings 26th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM 2018). Then, we compare the performance and power efficiency of these devices against same- generation CPUs and GPUs. Although this study includes some vision kernels such as: GICOV, Dilate, SRAD and MGVF, it was not mainly focused on benchmarking vision algorithms; it included other kernels for. 4x better energy e ciency compared to GPUs. These are loaded onto the LE1 system from the ARM A9 host via the Debug I/F (CTRL_S_AXI port on the vthreads_main_axi4_top_0 instance of Fig. of Computer & Informaon Sciences University of Delaware [email protected] This paper presents and characterizes Rodinia, a benchmark suite for heterogeneous computing. Rodinia: A Benchmark Suite for Heterogeneous Computing, Shuai Che (University of Virginia), Michael Boyer (University of Virginia), Jiayuan Meng (University of Virginia), David Tarjan (University of Virginia), Sang-Ha Lee (University of Virginia), Jeremy Sheaffer (University of Virginia), Kevin Skadron (University of Virginia). UPC++ is a C++11 library providing classes and functions that support Partitioned Global Address Space (PGAS) programming. Michael Pellauer , Michael Adler , Michel Kinsy , Angshuman Parashar , Joel Emer, HAsim: FPGA-based high-detail multicore simulation using time-division multiplexing, Proceedings of the 2011 IEEE 17th International Symposium on High Performance Computer Architecture, p. Intel has created Intel® FPGA SDK for OpenCL™ technology 1, which provides an alternative to HDL programming. Thus, while some of the applications in these benchmarks suites are applicable to studying the OpenCL to FPGA design flow, they require modifications to be useful. Three is not a crowd: A CPU-GPU-FPGA K-means implementation Marcos Canales 1, Jorge Cáncer 1, Denisa Constantinescu 2, Carlos Escuín 3 and Borja Pérez 4 1University of Zaragoza, Spain 2University of Malaga, Spain 3 Polytechnic University of Catalonia, Spain 4 University of Cantabria, Spain Introduction. GPUburn: A System to Test and Mitigate GPU Hardware Failures David Defour Universit´e de Perpignan Via Domitia, Laboratoire DALI - 54 avenue Paul Alduy 64000 Perpignan- France david. I graduated from the University of Virginia in August 2012 with a Ph. Each Janus module has one computational core and one host. Proof automation can substantially increase productivity in formal verification of complex systems. FCUDA-SoC: Platform Integration for Field-Programmable SoC with the CUDA-to-FPGA Compiler Tan Nguyen 1, Swathi Gurumani1, Kyle Rupnow , Deming Chen2 1 Advanced Digital Sciences Center, Singapore {tan. An OpenCL software compilation framework targeting an SoC-FPGA VLIW chip multiprocessor drawn from the AMD SDK and the Rodinia suites. OpenCL model on FPGA architecture. program acceleration in a heterogeneous computing environment using opencl, fpga, and cpu by herman noel hoffman a thesis submitted in partial fulfillment of the requirements for the degree of master of science in computer engineering university of rhode island 2017. •Rodinia benchmark suite [Cheet al. 0 as standard benchmarks. Processing Unit (GPU), a Central Processing Unit (CPU), and a Field-Programmable Gate Array (FPGA) or Many Integrated Core (MIC) device. Their evaluation. 4x over Rodinia implementation. Sadayappan, Ohio State University (OSU, USA), the research group of Louis-Noel Pouchet, Colorado State University (CSU, USA), and Inria CORSE research project. OpenCL™ (Open Computing Language) is a low-level API for heterogeneous computing that runs on CUDA-powered GPUs. FPGA Field Programmable Gate Array. OpenCL model on FPGA architecture. FPGA Field Programmable Gate Array. Bing doubles performance with FPGAs, will use them in 2015 68 posts • In this case a FPGA is less flexible than a general purpose CPU in that while it can be reconfigured to do many. We study multiple OpenCL kernels per benchmark, ranging from direct ports of the original GPU implementations to loop-pipelined kernels specifically optimized for FPGAs. When I started penning the title to this column, I began with "Have you got the globes?" and then the British comedy duo Morecambe and Wise popped into my mind, and I could imagine Ernie Wise asking this question and I could hear Eric Morecambe respond "No, I always walk this way!". 6 × improvement over fpga-maxJ and cpu, respectively. Back to the Top. Across 12 OpenCL benchmarks results demonstrate near-linear wall-clock performance improvement of 1. Exploiting DVFS for GPU Energy Management Testing was made using a set of benchmarks from the Rodinia suite and FPGA Field-Programmable Gate Array. For the SoC-FPGA implementation the tool-chain output results in two binary files, IRAM and DRAM for the kernel image and initialised data section. Janus is a modular, massively parallel, and reconfigurable FPGA-based computing system. School of Computing Science. To this end, this paper presents FlexCL, an analytical performance model for OpenCL workloads on exible FPGAs. player football video game using Verilog HDL and Xilinx software and synthesis on a Vertex 6 FPGA board with a keyboard and a VGA output. sg 2 Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, USA [email protected] Axel uses NNUS (non-uniform node, uniform system) architecture, where heterogeneous PUs (viz. Bekræftede arbejdsgivere. The benchmarks are executed on 40 LE1 configurations with 10 implemented on an SoC-FPGA and the remaining on a cycle-accurate simulator. FPGAs have a different compute model. DAS Day, @ TUDelft, 13/02/2013 16 Pro’s and Con’s Rodinia Graph 64K 393,216 64K Rodinia benchmark. Speedup of 73. 07027: Status: Project presented on July 8. GPUs can be used as specialized accelerators to improve network connectivity. We study multiple OpenCL kernels per benchmark, ranging from direct ports of the original GPU implementations to loop-pipelined. In [17], the authors optimized and ported a subset of the Rodinia benchmark suite to a Stratix® V FPGA using Intel FPGA SDK for OpenCL, and they compared the performance and energy efficiency between an Intel E5-2670 CPU and NVIDIA* K20c GPU. FPGA SUPPORT Rodinia is intended as a general benchmark suite for heterogeneous architectures but to the best of our knowledge was initially tested on CPUs and GPUs alone. Look for a new release. Read more>. The certification suite has been evaluated on an NVIDIA Kepler GPU and an Intel Xeon CPU with 8 cores. edu Henry Hoffmann [email protected] University of Massachusetts Lowell. This is the best published result to our best knowledge. To help architects study emerging platforms such as GPUs (Graphics Processing Units), Rodinia includes applications and kernels which target multi-core CPU and GPU platforms. GPUburn: A System to Test and Mitigate GPU Hardware Failures David Defour Universit´e de Perpignan Via Domitia, Laboratoire DALI - 54 avenue Paul Alduy 64000 Perpignan- France david. 2x (using 8 dual-issue cores) and on one case, super-linear improvement of 8. Further, because of extremely long FPGA synthesis times, the overhead of recompiling the host code for each compilation of FPGA kernel code is relatively inexpensive. Check-list (artifact meta information): • Algorithm: Benchmarks from the Rodinia Benchmark Suite 3. - Analysis of the Rodinia benchmark for FPGA implementation potential (complete implementations and CPU-to-FPGA offloading) - Architectural exploration and development of FPGA IP cores of chosen. Oren_Segal/[email protected] student. Software dependencies Describe any specific OS and software packages required to evaluate your artifact. A hypothesis is proposed and uses static off-line partitioning and mapping of heterogeneous tasks to improve space sharing on FPGA. MERLOT: Architectural Support for Energy-Efficient Real-time Processing in GPUs Muhammad Santriaji [email protected] The CHERI architecture allows pointers to be implemented as capabilities (rather than integer virtual addresses) in a manner that is compatible with, and strengthens, the semantics of the C language. Issuu is a digital publishing platform that makes it simple to publish magazines, catalogs, newspapers, books, and more online. We study multiple OpenCL kernels per benchmark, ranging from direct ports of the original GPU implementations to loop-pipelined kernels specifically optimized for FPGAs. fr Eric Petit Universit´e de Versailles Saint-Quentin, Laboratoire PRISM - 45 avenue des Etats-Unis 78035 Versailles - France eric. RadICS Platform. 8594 2009-12-22 The mass of asymptotically hyperbolic Riemannian manifolds Piotr T. 30x over 8 of 19 kernels that were determined safe to coarsen. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. GPU and FPGA-based clouds have illustrated improvements in power and performance by accelerating compute-intensive workloads. Term: WS 2019/20 + SS 2020: Program: Computer Science Master's Computer Engineering Master's: Lecture number: L. Janus is a modular, massively parallel, and reconfigurable FPGA-based computing system. We provide a simple five-step strategy for FPGA accelerator designs that can be easily understood and mastered by software programmers, and present. 2x (using 8 dual-issue cores) and on one case, super-linear. word(s) sdev freq; degreesc: 14. Rodinia [8] and Parboil [49]. We offer expertise in FPGA/ASIC Design, Board Design and Layout, Device Drivers, and all other support Softwares and Documentations. Their evaluation. Across 12 OpenCL benchmarks results demonstrate near-linear wall-clock performance. Abhay has 5 jobs listed on their profile. The testing flows will be discussed thoroughly along with many optimization decisions. Although this study includes some vision kernels such as: GICOV, Dilate, SRAD and MGVF, it was not mainly focused on benchmarking vision algorithms; it included other kernels for. The certification suite has been evaluated on an NVIDIA Kepler GPU and an Intel Xeon CPU with 8 cores. 2x (using 8 dual-issue cores) and on one case, super-linear improvement of 8. Optimized HLS implementation on real FPGA devices 3 Related Work Benchmark Suite Language Primary Focus #Benchmarks CHStone [Hara et. 71 over Intel's state-of-the-art implementations on Parboil and Rodinia ii. At small grid-sizes though, the overhead of handling multiple streams per input and output array dominates and we have relatively less improvement or even a decrease. We use the polyhedral compiler to generate highly optimized OpenCL code for a set of standard benchmark suites (Rodinia and SHOC), image processing kernels, and for two DSL compilers: a linear algebra (BLAS) DSL compiler and a DSL for signal processing radar applications (SpearDE). Using the OpenCL API, developers can launch compute kernels written using a limited subset of the C programming language on a GPU. IS860/CO403 - COURSE PROJECTS Suggested Course Projects • Develop a 3D NoC Architecture in SystemC. FPGA上で動作するOpenCLプログラムを開発することができる。 各SDKには、標準OpenCL API用のC/C++言語用ヘッダーなどのほか、ベンダーごとに拡張された機能を使うためのライブラリなども含まれるため、ハードウェア ベンダーやOSに依存しないOpenCLプログラムを. See the complete profile on LinkedIn and discover Ramkumar's connections and jobs at similar companies. CDSC-GR: a CnC-inspired Graph Representation CnC 2013 -- September 24, 2013 Alina 1Sbirlea , Zoran Budimlic1, Jason Cong2, Zhuo Li2, Louis-Noel Pouchet2, Vivek 1Sarkar and Mo Xu2 1 Rice University 2 University of California Los Angeles. Describe any specific hardware and its features strictly required to evaluate your artifact (vendor, CPU/GPU/FPGA, number of processors/cores, interconnect, memory, hardware counters, etc). FCUDA-SoC: Platform Integration for Field-Programmable SoC with the CUDA-to-FPGA Compiler Tan Nguyen 1, Swathi Gurumani1, Kyle Rupnow , Deming Chen2 1 Advanced Digital Sciences Center, Singapore {tan. Software dependencies Describe any specific OS and software packages required to evaluate your artifact. By adopting applications from Rodinia and SHOC, and including newly written applications with special focus on DNNs, Mirovia better characterizes. Abstract: We evaluate the power and performance of the Rodinia benchmark suite using the Altera SDK for OpenCL targeting a Stratix V FPGA against a modern CPU and GPU. Such as the other GPU benchmark suites such as Rodinia, CUDA-SDK, ISPASS benchmark suite, and Parboil. , malicious or benign []. OpenCL model on FPGA architecture. A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads S Che, JW Sheaffer, M Boyer, LG Szafaryn, L Wang, K Skadron 2010 IEEE International Symposium on Workload Characterization (IISWC) , 2010. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: