Probing transverse momentum dependent gluon distribution functions from hadronic quarkonium pair production

The inclusive hadronic production of $\eta_Q$($\eta_c$ or $\eta_b$) pair is proposed to extract the transverse momentum dependent(TMD) gluon distribution functions. We use nonrelativistic QCD(NRQCD) for the production of $\eta_Q$. Under nonrelativistic limit TMD factorization for this process is assumed to make a lowest order calculation. For unpolarized initial hadrons, unpolarized and linearly polarized gluon distributions can be extracted by studying different angular distributions.Comment: 7 pages, 3 figures, 1 tabl

AutoAccel: Automated Accelerator Generation and Optimization with Composable, Parallel and Pipeline Architecture

CPU-FPGA heterogeneous architectures are attracting ever-increasing attention in an attempt to advance computational capabilities and energy efficiency in today's datacenters. These architectures provide programmers with the ability to reprogram the FPGAs for flexible acceleration of many workloads. Nonetheless, this advantage is often overshadowed by the poor programmability of FPGAs whose programming is conventionally a RTL design practice. Although recent advances in high-level synthesis (HLS) significantly improve the FPGA programmability, it still leaves programmers facing the challenge of identifying the optimal design configuration in a tremendous design space. This paper aims to address this challenge and pave the path from software programs towards high-quality FPGA accelerators. Specifically, we first propose the composable, parallel and pipeline (CPP) microarchitecture as a template of accelerator designs. Such a well-defined template is able to support efficient accelerator designs for a broad class of computation kernels, and more importantly, drastically reduce the design space. Also, we introduce an analytical model to capture the performance and resource trade-offs among different design configurations of the CPP microarchitecture, which lays the foundation for fast design space exploration. On top of the CPP microarchitecture and its analytical model, we develop the AutoAccel framework to make the entire accelerator generation automated. AutoAccel accepts a software program as an input and performs a series of code transformations based on the result of the analytical-model-based design space exploration to construct the desired CPP microarchitecture. Our experiments show that the AutoAccel-generated accelerators outperform their corresponding software implementations by an average of 72x for a broad class of computation kernels

Hardness Results for Structured Linear Systems

We show that if the nearly-linear time solvers for Laplacian matrices and their generalizations can be extended to solve just slightly larger families of linear systems, then they can be used to quickly solve all systems of linear equations over the reals. This result can be viewed either positively or negatively: either we will develop nearly-linear time algorithms for solving all systems of linear equations over the reals, or progress on the families we can solve in nearly-linear time will soon halt