Upgrade Letter of Intent High Level Trigger Thorsten Kollegger ALICE | Offline Week |

Requirements Focus of ALICE upgrade on physics probes requiring high statistics: sample 10 nb -1 Online System Requirements Sample full 50kHz Pb-Pb interaction rate (current limit at ~500Hz, factor 100 increase)  ~1.1 TByte/s detector readout However: storage bandwidth limited to ~20 GByte/s many physics probes have low S/B: classical trigger/event filter approach not efficient 2ALICE | Offline Week | | Thorsten Kollegger

3 Main physics topics, at the LHC uniquely accessible with the ALICE detector: measurement of heavy-flavour transport parameters: diffusion coefficient – azimuthal anisotropy and R AA in-medium thermalization and hadronization – meson-baryon mass dependence of energy loss – R AA study of QGP properties via transport coefficients (  /s, q) J/ ,  ’, and  c states down to zero p t in wide rapidity range yields and transverse momentum spectra – R AA, elliptic flow density dependence – central vs. forward production statistical hadronization vs. dissociation/recombination ˆ Physics Motivation Slide from Karel Safarik ALICE | Offline Week | | Thorsten Kollegger

4 measurement of low-mass and low-p t di-leptons chiral symmetry restoration – vector-meson spectral function disappearance of vacuum condensate and generation of hadron masses QGP thermal radiation – low-mass di-lepton continuum space-time evolution of the QGP – radial and elliptic flow of emitted radiation Jet quenching and fragmentation jet energy recuperation at very low p t heavy-flavour tagged jets, gluon vs. quark induced jets heavy-flavour produced in fragmentation particle identified fragmentation functions Heavy-nuclear states high statistics mass-4 and -5 (anti-)hypernuclei search for H-dibaryon,  n bound state, etc. Physics Motivation ALICE | Offline Week | | Thorsten Kollegger Slide from Karel Safarik

Why not triggering? 5 Triggering on D 0, D s and Λ c (p T >2 Gev/c)  ~ 36 rate... Slide from Luciano Musa ALICE | Offline Week | | Thorsten Kollegger

Strategy Data reduction by (partial) online reconstruction and compression Store only reconstruction results, discard raw data Demonstrated with TPC clustering since Pb-Pb 2011 Optimized data structures for lossless compression Algorithms designed to allow for offline reconstruction passes with improved calibrations  Implies much tighter coupling between online and offline reconstruction software 6ALICE | Offline Week | | Thorsten Kollegger

Event Size Expected data sizes for minimum bias Pb-Pb collisions at full LHC energy 7 Detector Event Size (MByte) After Zero Suppression After Data Compression TPC TRD ITS Others Total ALICE | Offline Week | | Thorsten Kollegger

TPC Data Reduction First steps up to clustering on FEE/FPNs (RORC FPGA) Further steps require full event reconstruction on EPNs, pattern recognition requires only coarse online calibration 8 Data Format Data Reduction Factor Event Size (MByte) Raw Data1700 FEEZero Suppression3520 HLT Clustering & Compression5-7~3 Remove clusters not associated to relevant tracks 21.5 Data format optimization2-3<1 ALICE | Offline Week | | Thorsten Kollegger

Data MemberHLT Cluster FormatOptimized Padrow Number UShort16 bit6 bit Pad PositionFloat32 bit14 bit TimebinFloat32 bit15 bit Width PadFloat32 bit8 bit Width TimeFloat32 bit8 bit Total ChargeShort16 bit Max ChargeShort16 bit10 bit Reduction of data size/cluster: 22 Byte -> 10 Byte Float to Fixed-Point convertion, size according to detector resolution TPC Data Reduction ALICE | Offline Week | | Thorsten Kollegger9

Lossless data compression with Huffman code (entropy encoding) Data members transformed to increase performance: e.g. Padrow Number =>  row(i) = row(i) – row(i-1) Entropy reduced from ~6 to 1.1 ALICE | Offline Week | | Thorsten Kollegger10 TPC Data Reduction

Overall data size to tape reduced by factor 4.3 Used in Pb+Pb 2011, p+p standard ALICE data format Further reduction possible by transforming pad, time coordinates ALICE | Offline Week | | Thorsten Kollegger11 TPC Data Reduction

First steps up to clustering on FEE/FPNs (RORC FPGA) Further steps require full event reconstruction on EPNs, pattern recognition requires only coarse online calibration 12 Data Format Data Reduction Factor Event Size (MByte) Raw Data1700 FEEZero Suppression3520 HLT Clustering & Compression5-7~3 Remove clusters not associated to relevant tracks 21.5 Data format optimization2-3<1 ALICE | Offline Week | | Thorsten Kollegger

Discard clusters not assigned to tracks (or in the track vincinity) - Requires online calibration (at least coarse one) - Allows later offline re-production Alternative: identify background clusters ALICE | Offline Week | | Thorsten Kollegger13 Further data reduction

Processing Power Estimate for online systems based on current HLT processing power - ~2500 cores in ~200 nodes -108 FPGAs on H-RORCs for local preprocessing -TPC clusterfinding: 1 FPGA equivalent to ~80 CPU cores - 64 GPGPUs for tracking (NVIDIA GTX480 + GTX580) Scaling to 50 kHz rate to estimate requirements - ~ cores - additional processing power by FPGAs + GPGPUs  nodes in 2018 with multicores 14ALICE | Offline Week | | Thorsten Kollegger

Name of Task (Initialization) Combinatorial Part (Cellular Automation) Neighbors Finding Evolution Kalman Filter Part Tracklet Construction Tracklet Selection (Tracklet Output) Algorithm implemented as multithreaded CPU and CUDA GPU version ALICE | Offline Week | | Thorsten Kollegger15 HLT TPC Tracking

3-fold speedup of GPU compared to optimized CPU version on 6 cores ALICE | Offline Week | | Thorsten Kollegger16 HLT TPC Tracking

Consistency between GPU and CPU version of tracker HLT Tracking Performance ALICE | Offline Week | | Thorsten Kollegger17 Active GPU Threads using Dynamic Scheduling Active GPU Threads: 67% threads time

ALICE | Offline Week | | Thorsten Kollegger18 HLT Tracking Performance Efficiency/Clone/Fake rate calculation merged with PWG-PP/TPC code - Under review by TPC group Old HLT efficiency macro

Summary After the upgrade: Store only reconstruction results, discard raw data Requires online calibration Algorithms designed to allow for offline reconstruction passes with improved calibrations  Implies much tighter coupling between online and offline reconstruction software 19ALICE | Offline Week | | Thorsten Kollegger

20ALICE | Offline Week | | Thorsten Kollegger

Backup - Processing Power Estimate of processing power based on scaling by Moore’s law However: no increase in single core clock speed, instead multi/many-core  Reconstruction software needs to adapt to full use resources 21 Picture from Herb Sutte: The Free Lunch Is Over A Fundamental Turn Toward Concurrency in Software Dr. Dobb's Journal, 30(3), March 2005 (updated) ALICE | Offline Week | | Thorsten Kollegger