(Challenges of parallelizing code, motivations for parallel chips, processor basics)

(Forms of parallelism: multi-core, SIMD, and multi-threading)

(Finish up multi-threaded and latency vs. bandwidth. ISPC programming, abstraction vs. implementation)

(Ways of thinking about parallel programs, thought process of parallelizing a program in data parallel and shared address space models)

(Achieving good work distribution while minimizing overhead, scheduling Cilk programs with work stealing)

(Message passing, async vs. blocking sends/receives, pipelining, increasing arithmetic intensity, avoiding contention)

(CUDA programming abstractions, and how they are implemented on modern GPUs)

(Data-parallel operations like map, reduce, scan, prefix sum, groupByKey)

(Producer-consumer locality, RDD abstraction, Spark implementation and scheduling)

(Efficiently scheduling DNN layers, mapping convs to matrix-multiplication, transformers, layer fusion)

(Definition of memory coherence, invalidation-based coherence using MSI and MESI, false sharing)

(Relaxed consistency models and their motivation, acquire/release semantics)

(Fine-grained synchronization via locks, basics of lock-free programming: single-reader/writer queues, lock-free stacks, the ABA problem, hazard pointers)

(Performance/productivity motivations for DSLs, case studies on several DSLs)

(Motivation for transactions, design space of transactional memory implementations.)

(Finishing up transactional memory focusing on implementations of STM and HTM.)

(Energy-efficient computing, motivation for heterogeneous processing, fixed-function processing, FPGAs, mobile SoCs)

(How DRAM works, suggestions for post-cs149 topics)