Single Core Intel Research Articles

UMC-PET: a fast and flexible Monte Carlo PET simulator

Objective. The GPU-based Ultra-fast Monte Carlo positron emission tomography simulator (UMC-PET) incorporates the physics of the emission, transport and detection of radiation in PET scanners. It includes positron range, non-colinearity, scatter and attenuation, as well as detector response. The objective of this work is to present and validate UMC-PET as a a multi-purpose, accurate, fast and flexible PET simulator. Approach. We compared UMC-PET against PeneloPET, a well-validated MC PET simulator, both in preclinical and clinical scenarios. Different phantoms for scatter fraction (SF) assessment following NEMA protocols were simulated in a 6R-SuperArgus and a Biograph mMR scanner, comparing energy histograms, NEMA SF, and sensitivity for different energy windows. A comparison with real data reported in the literature on the Biograph scanner is also shown. Main results. NEMA SF and sensitivity estimated by UMC-PET where within few percent of PeneloPET predictions. The discrepancies can be attributed to small differences in the physics modeling. Running in a 11 GB GeForce RTX 2080 Ti GPU, UMC-PET is ∼1500 to ∼2000 times faster than PeneloPET executing in a single core Intel(R) Xeon(R) CPU W-2155 @ 3.30 GHz. Significance. UMC-PET employs a voxelized scheme for the scanner, patient adjacent objects (such as shieldings or the patient bed), and the activity distribution. This makes UMC-PET extremely flexible. Its high simulation speed allows applications such as MC scatter correction, faster SRM estimation for complex scanners, or even MC iterative image reconstruction.

A FPGA-based accelerated architecture for the Continuous GRASP

This work proposes a FPGA-based architecture for accelerating the Continuous GRASP (C-GRASP), a prominent metaheuristic for solving continuous optimization problems. Although FPGA implementations have been proposed for other metaheuristics, nothing has been done for C-GRASP. We conduct a comprehensive set of experiments on well-known hard test problems, and compare our FPGA architecture with C-GRASP implementations running on four other architectures: a single-core ARM Cortex A9-based, a single-core Intel i7-based, a multi-core Intel i7-based, and a GPU-based. Experimental results demonstrate that the proposed architecture outperforms the others in terms of performance-to-power-efficiency. For instance, it is on average 3x faster and 15x less power consuming than the single-core Intel i7-based implementation. Moreover, we introduce a model-based method that helps designers with little experience in FPGA development to use our high-performance architecture to optimize their problem. Our solution is a very interesting option for the emerging technology of FPGA-based data centers (e.g, Microsoft’s Catapult project), and also for resource-constrained embedded systems (e.g., drones).

FPGA Online Tracking Algorithm for the PANDA Straw Tube Tracker

A novel FPGA based online tracking algorithm for helix track reconstruction in a solenoidal field, developed for the PANDA spectrometer, is described. Employing the Straw Tube Tracker detector with 4636 straw tubes, the algorithm includes a complex track finder, and a track fitter. Implemented in VHDL, the algorithm is tested on a Xilinx Virtex-4 FX60 FPGA chip with different types of events, at different event rates. A processing time of 7 $\mu$s per event for an average of 6 charged tracks is obtained. The momentum resolution is about 3\% (4\%) for $p_t$ ($p_z$) at 1 GeV/c. Comparing to the algorithm running on a CPU chip (single core Intel Xeon E5520 at 2.26 GHz), an improvement of 3 orders of magnitude in processing time is obtained. The algorithm can handle severe overlapping of events which are typical for interaction rates above 10 MHz.

The efficient implementation of correction procedure via reconstruction with graphics processing unit computing

Computational fluid dynamics (CFD) has long been a useful tool to model fluid flow problems across many engineering disciplines, and while problem size, complexity, and difficulty continue to expand, the demands for robustness and accuracy grow. Furthermore, generating high-order accurate solutions has escalated the required computational resources, and as problems continue to increase in complexity, so will computational needs such as memory requirements and calculation time for accurate flow field prediction. To improve upon computational time, vast amounts of computational power and resources are employed, but even over dozens to hundreds of central processing units (CPUs), the required computational time to compute solutions can be weeks, months, or longer, which is particularly true when generating high-order accurate solutions over large computational domains. One response to lower the computational time for CFD problems is to implement current CFD solvers on graphical processing units (GPUs). GPUs have illustrated the ability to solve problems orders of magnitude faster than their CPU counterparts with identical accuracy. The goal of the presented work is to combine a CFD solver and GPU computing with the intent to solve complex problems at a high-order of accuracy while lowering the computational time required to generate the solution. The CFD solver should have high-order spacial capabilities to evaluate small fluctuations and fluid structures not generally captured by lower-order methods (2nd and 1st order) and be efficient for the GPU architecture. This research combines the high-order Correction Procedure via Reconstruction (CPR) method for explicit time-stepping with compute unified device architecture (CUDA) from NVIDIA to reach these goals. In addition, the study demonstrates accuracy of the developed solver by comparing results with other solvers and exact solutions. Finally, to illustrate speed-ups, a single core Intel Xeon X5650 at 2.67GHz is compared against a Tesla C2070 and a K20x, where a maximum speed-up of nearly 70x is demonstrated.

Development of parallel direct simulation Monte Carlo method using a cut-cell Cartesian grid on a single graphics processor

This study developed a parallel two-dimensional direct simulation Monte Carlo (DSMC) method using a cut-cell Cartesian grid for treating geometrically complex objects using a single graphics processing unit (GPU). Transient adaptive sub-cell (TAS) and variable time-step (VTS) approaches were implemented to reduce computation time without a loss in accuracy. The proposed method was validated using two benchmarks: 2D hypersonic flow of nitrogen over a ramp and 2D hypersonic flow of argon around a cylinder using various free-stream Knudsen numbers. We also detailed the influence of TAS and VTS on computational accuracy and efficiency. Our results demonstrate the efficacy of using TAS in combination with VTS in reducing computation times by more than 10×. Compared to the throughput of a single core Intel CPU, the proposed approach using a single GPU enables a 13–35× increase in speed, which varies according to the size of the problem and type of GPU used in the simulation. Finally, the transition from regular reflection to Mach reflection for supersonic flow through a channel was simulated to demonstrate the efficacy of the proposed approach in reproducing flow fields in challenging problems.