goSLP: Globally Optimized Superword Level Parallelism Framework

Modern microprocessors are equipped with single instruction multiple data (SIMD) or vector instruction sets which allow compilers to exploit superword level parallelism (SLP), a type of fine-grained parallelism. Current SLP auto-vectorization techniques use heuristics to discover vectorization opportunities in high-level language code. These heuristics are fragile, local and typically only present one vectorization strategy that is either accepted or rejected by a cost model. We present goSLP, a novel SLP auto-vectorization framework which solves the statement packing problem in a pairwise optimal manner. Using an integer linear programming (ILP) solver, goSLP searches the entire space of statement packing opportunities for a whole function at a time, while limiting total compilation time to a few minutes. Furthermore, goSLP optimally solves the vector permutation selection problem using dynamic programming. We implemented goSLP in the LLVM compiler infrastructure, achieving a geometric mean speedup of 7.58% on SPEC2017fp, 2.42% on SPEC2006fp and 4.07% on NAS benchmarks compared to LLVM's existing SLP auto-vectorizer.Comment: Published at OOPSLA 201

Spatial variation of water supply and demand in Sri Lanka

Water supplyWater demandRiver basinsRunoffIrrigation efficiency

A parallel Viterbi decoder for block cyclic and convolution codes

We present a parallel version of Viterbi's decoding procedure, for which we are able to demonstrate that the resultant task graph has restricted complexity in that the number of communications to or from any processor cannot exceed 4 for BCH codes. The resulting algorithm works in lock step making it suitable for implementation on a systolic processor array, which we have implemented on a field programmable gate array and demonstrate the perfect scaling of the algorithm for two exemplar BCH codes. The parallelisation strategy is applicable to all cyclic codes and convolution codes. We also present a novel method for generating the state transition diagrams for these codes

Stability of the Gains of the STAR Endcap Calorimeter from 2009 to 2012

The Solenoid Tracker at RHIC (STAR) experiment, based at Brookhaven National Laboratory\u27s Relativistic Heavy Ion Collider, uses polarized-proton collisions to investigate sea quark and gluon contributions to the known proton spin. The STAR detector\u27s Endcap Electromagnetic Calorimeter (EEMC) is of particular interest in this experiment because it covers a kinematic region, which is sensitive to gluons carrying a low fraction of the proton momentum, where the gluon spin is almost entirely unconstrained. The EEMC is located in the intermediate pseudorapidity range, 1 \u3c η \u3c 2, and measures the electromagnetic energy of particles produced by the collisions using a lead-scintillator sampling calorimeter. The calorimeter consists of several layers that include pre-shower, shower maximum, tower, and post-shower detectors. In these detectors, the energy gains, which convert a measured signal into an energy deposition, have been determined using data taken from the years 2009, 2011, and 2012. These gains will be analyzed and studied in order to understand the calibration of the detector

Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks

Predicting the number of clock cycles a processor takes to execute a block of assembly instructions in steady state (the throughput) is important for both compiler designers and performance engineers. Building an analytical model to do so is especially complicated in modern x86-64 Complex Instruction Set Computer (CISC) machines with sophisticated processor microarchitectures in that it is tedious, error prone, and must be performed from scratch for each processor generation. In this paper we present Ithemal, the first tool which learns to predict the throughput of a set of instructions. Ithemal uses a hierarchical LSTM--based approach to predict throughput based on the opcodes and operands of instructions in a basic block. We show that Ithemal is more accurate than state-of-the-art hand-written tools currently used in compiler backends and static machine code analyzers. In particular, our model has less than half the error of state-of-the-art analytical models (LLVM's llvm-mca and Intel's IACA). Ithemal is also able to predict these throughput values just as fast as the aforementioned tools, and is easily ported across a variety of processor microarchitectures with minimal developer effort.Comment: Published at 36th International Conference on Machine Learning (ICML) 201

Strategic Analyses of the National River Linking Project (NRLP) of India, Series 4. Water productivity improvements in Indian agriculture: potentials, constraints and prospects

Water productivityWater use efficiencyMultiple useIrrigation practicesIrrigation systemsWater qualityWater allocationCerealsCrop yieldLivestockMilk productionEconomic aspects

Policy interfacing and irrigation development in Tamil Nadu

AgroclimatologyGroundwater irrigationWellsTank irrigationCanalsIrrigation systemsPolicyIrrigated landWater use efficiency