FIAS Scientific Report 2011 - Frankfurt Institute for Advanced Studies ...

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ALICE HLT TPC Tracking on GPU Collaborators: D. Rohr 1 , S. Gorbunov 1 , V. Lindenstruth 1 1 Frankfurt Institute for Advanced Studies ALICE is one of the major four experiments of the Large Hadron Collider at CERN in Geneva, which today is the most powerful particle accelerator. The ALICE High Level Trigger is capable of a full real-time event reconstruction based on the raw data obtained from the detector. In this process, reconstruction of particle trajectories (tracking) is the most compute intensive task – especially for heavy ion collisions with the detector being filled with several thousands of particles. Since many particles can be tracked in parallel, GPUs can be employed to accelerate the tracking. The ALICE HLT tracker combines the cellular automaton principle with the Kalman filter for the track fit. From the beginning it was designed with parallelism in mind and a GPU enabled version of the tracker has been created [I]. Both CPU and GPU tracker essentially share the same common source code for the tracker, which is encapsulated by two specialized wrappers for both hardware architectures. GPU and CPU tracker have been proven to produce identical results. Marginal differences arise only from different rounding due to non-associative floating-point arithmetic but there are no influences of concurrency on the tracking result. The ALICE GPU tracker was operated during the heavy ion phases in November 2010 and November 2011. The below figure shows a screenshot of the ALICE online event display during the first physics run with active GPU tracker. One particular challenge of the implementation is to ensure continuous GPU utilization. The ALICE GPU tracker accomplishes this with a pipeline, which overlaps pre- and postprocessing on CPU and tracking on GPU of multiple sectors of the detector. Since a single CPU core cannot keep step with the GPU, the pipeline is multithreaded. The below figure visualizes the parallel processing of several subtasks of tracking for different sectors of the detector. DMA GPU CPU 1 CPU 2 CPU 3 Tasks: Preprocessing Neighbor Finding Tracklet Construction Tracklet Selection Postprocessing Related publications in 2011: 1) S. Gorbunov, D. Rohr, et al. ALICE HLT high speed tracking on GPU, IEEE Transactions on Nuclear Science 58 (2011) 1845 116 Time
Arithmetic over Galois fields on modern processor architectures Collaborators: S. Kalcher 1 , V. Lindenstruth 1 1 Frankfurt Institute for Advanced Studies Galois fields (also called finite fields) play an essential role in the areas of cryptography and coding theory. They are the foundation of various error- and erasure-correcting codes and therefore central to the design of reliable storage systems. The efficiency and performance of these systems depend considerably on the implementation of Galois field arithmetic, in particular on the implementation of the multiplication. In current software implementations multiplication is typically performed by using pre-calculated lookup tables for the logarithm and its inverse or even for the full multiplication result. However, today the memory subsystem has become one of the main bottlenecks in commodity systems and relying on large in-memory data structures accessed from inner loop code can severely impact the overall performance and deteriorate scalability. In this study, we implemented a variant of Galois field multiplication on modern processor architectures without using lookup tables. Instead we trade computation for memory references and perform full polynomial multiplication with modular reduction using the generator polynomial of the Galois field. The basis for the vectorized polynomial multiplication is the well known Russian peasant multiplication algorithm (also known as Egyptian multiplication). Integer multiplication can be performed by only using integer doubling, halving, and addition. The classical multiplication tables for numbers from one to ten are not required. Since only XOR, AND and shift operations are used, the algorithm can be vectorized with the integer part of the Streaming SIMD Extensions (SSE). The presented implementation uses SSE compiler API extensions, the so called intrinsics. The core multiplication code uses only integer SIMD instructions available in SSE version 2, but some instructions from SSE version 3 and 4 (shuffle, min/max, extract) can be beneficial for reducing the number of loop cycles. In addition to the SIMD instruction extensions of commodity processors, the algorithm is well suited for execution on graphics processing units (GPUs), which have evolved rapidly in recent years as general purpose co-processors. The figures show the results of the vectorized (left) and GPU (right) implementation of the polynomial multiplication algorithm in GF(2 16 ), each compared to the table-based approach. throughput [Mibit/s] 60000 50000 40000 30000 20000 10000 0 Multiplication troughput in GF(2 16 ) dynamic argument unrolled conditional lookup-based multiplication 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 number of loop cycles gfmult throughput [Mibit/s] 16000 14000 12000 10000 8000 6000 4000 2000 10 0 0 10 1 10 2 10 3 block size [KiB] 1 stream, poly 2 streams, poly 4 streams, poly 4 streams, table Left: Multiplication throughput for the vectorized implementation depending on the actual number of required loop cycles. Right: Multiplication throughput of the GPU implementation for different numbers of overlapping execution streams. Related publications in 2011: 1) Sebastian Kalcher, Volker Lindenstruth, Accelerating Galois field arithmetic for Reed-Solomon erasure codes in storage applications, pp.290-298, 2011 IEEE International Conference on Cluster Computing, 2011 117 10 4 10 5
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FIAS Scientific Report 2011
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FIAS Scientific Report 2011 Table o
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Physics Research highlights 2011 To
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1. Partner Research Centers 9
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ExtreMe Matter Institute EMMI by Ca
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Bernstein Focus Neurotechnology Fra
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2. Graduate Schools 15
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participants reported on their proj
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Courses offered at FIGSS Summer Sem
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3. FIAS Scientific Life 21
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28.07.2011 Prof. Dr. Victor Flambau
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Conferences and meetings (co)organi
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4. Research Reports 4.1 Nuclear Phy
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Hydrodynamics of a quark droplet Co
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Production of hyper-nuclei in react
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On production and properties of mul
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Monte Carlo modeling microdosimetry
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Monte Carlo simulation of the spall
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Dimuon radiation within a (3+1)d hy
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Initial state anisotropies and thei
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Chiral hadronic model including res
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Trace anomaly and the vector coupli
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Dileptons from the strongly interac
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Space-time evolution of the magneti
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Extreme isospin in heavy nuclei Col
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Production of heavy and superheavy
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The phase diagram in T -µB-Nc spac
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Critical Zeeman Fields for Unitary
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BCS-BEC Crossover in 2D Fermi Gases
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Applications of a chiral SU(3) mode
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The phase diagram of black holes in
Page 65 and 66: Black holes in short scale modified
Page 67 and 68: Fractal dimensions and micro-struct
Page 69 and 70: Untangling the interactions between
Page 71 and 72: Stimulus information coded by spike
Page 73 and 74: Non-stationarity of neuronal activi
Page 75 and 76: Timescale of information processing
Page 77 and 78: Discovery of agency in 6 and 8-mont
Page 79 and 80: Power spectra of the natural input
Page 81 and 82: Self-organization in recurrent neur
Page 83 and 84: Learning mechanisms underlying visu
Page 85 and 86: Emerging Bayesian priors in a self-
Page 87 and 88: Non-linear generative models and th
Page 89 and 90: Preference elicitation and Bayesian
Page 91 and 92: 4.3 Biology, Chemistry, Molecules,
Page 93 and 94: DNA unzipping Collaborators: S.N. V
Page 95 and 96: Statistical mechanics of protein fo
Page 97 and 98: Phase transitions in large clusters
Page 99 and 100: Photo-processes in fullerenes and e
Page 101 and 102: Assessment of complex DNA damage Co
Page 103 and 104: Novel light sources: Crystalline un
Page 105 and 106: Monte-Carlo code for channeling dyn
Page 107 and 108: Programs for many-body descriptions
Page 109 and 110: Are 12 C radiation effects in liver
Page 111 and 112: Objective identification of residue
Page 113 and 114: Structural basis for the dual RNA-r
Page 115: The ALICE High Level Trigger Collab
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Page 121 and 122: How stable are transport model resu
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Page 125 and 126: Conference and Seminar Talks 2011
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Page 161: The success of FIAS would not have
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