Reduced-Error Pruning Split data into training and validation set Do until further pruning is harmful: 1. Evaluate impact on validation set of pruning each possible node (plus those below it) 2. Greedily remove the one that most improves validation set accuracy produces smallest version of most accurate subtree · What if data is limited?

• Study increasingly sophisticated parallel merge kernels • Observe the combined effects of data - dependent execution and a lack of data parallelism on GPU algorithm design

• To learn to regularize irregular data with – Limiting variations with clamping – Sorting – Transposition • To learn to write a high-performance SpMV kernel based on JDS transposed format • To learn the key techniques for compacting input data in parallel sparse methods for reduced consumption of memory bandwidth – Better utilization of on-chip memory – Fewer bytes transferred to on-chip memory – Better utilization of global memory – Challenge: retaining regularity

Introduction to Heterogeneous Parallel Computing CUDA C vs. CUDA Libs vs. OpenACC Memory Allocation and Data Movement API Functions Data Parallelism and Threads

 理解数据链路层服务原理:  差错检测和纠正  共享广播信道: 多址接入  链路层编址  可靠传输、流量控制：done!  链路层实现  以太网  虚拟局域网 5.1 introduction, services 5.2 error detection, correction 5.3 multiple access protocols 5.4 LANs ▪ addressing, ARP ▪ Ethernet ▪ switches ▪ VLANS 5.5 link virtualization 5.6 data center networking 5.7 a day in the life of a web request

Our task How to estimate and test discount factor model. Ep,=E(m (data 1, parameter)) 1. Bring an asset pricing model to data to estimate free parameters. For example, parameter,yinm=B(c+1/c)-y Or the b in m=b f 2. Evaluate the model, is it a good model or not? Is another model better?

 Review  Consumption under Certainty  Consumption under Uncertainty  Empirical Application • Aggregate data • Household data  Optimal Lifetime Portfolio

Component Factors of the Time-Series Model Smoothing of Data Series Moving Averages Exponential Smoothing Least Square Trend Fitting and Forecasting Linear, Quadratic and Exponential Models Autoregressive Models Choosing Appropriate Models Monthly or Quarterly Data

Questions: what can -do or cannot-do of a von Neumann machine Good at Not so good at Fast arithmetic Interacting with noisy data or data from the environment Doing precisely what the Massive parallelism programmer programs them to do Fault tolerance

8.1.1 Three-Address Code 8.1.2 Data Structures for theImplementation of Three-Address Code