Tensor and Linear Algebra are powerful tools with applications in machine learning, engineering, science, and data analytics. The massive growth of data in these applications makes performance critical. For applications that use sparse tensors, where most components are zeros, programmers must choose between libraries with hand-optimized implementations of select operations and generalized software systems with poor performance.

Memory and communication are the primary reasons that our time-to-solution is no better than it currently is ... the memory system is slow; the communication overhead is high; and yet a significant amount of research is still focused on increasing processor performance, rather than decreasing (the cost of) data movement. I will discuss recent & near-term memory-system technologies including high-bandwidth DRAMs and nonvolatile main memories, as well as the impact of tomorrow's memory technologies on tomorrow's applications and operating systems.