HPC Post Doc

Overview

• Identifying both existing and novel basic operations relevant to optimization platforms;
• Speed-of-light analyses of both existing and newly identified basic operations that can a) identify fundamental performance bottlenecks, b) accurately predict scalability properties (e.g., iso-efficiency), c) predict trade-off effects (e.g., memory vs. communication), and d) predict what combination of devices (classic CPUs or specialized accelerators and how many) would lead to the highest efficiency solves;
• The design and prototyping of highly scalable, highly efficient, and highly productive software systems that lie at the foundation of our next-generation optimization platform.

• Design and implement novel basic operators required for our optimization platform;
• Analyze specific algorithms for basic operators and establish fundamental limits in models of parallel computation that account not only for classic work (flops) and compute power (flop/s), but also account for data reuse, and memory throughput & access latencies;
• Following, as appropriate, cache-aware or cache-oblivious paradigms, as well as standard HPC paradigms for shared- and distributed-memory parallelization, vectorization, etc.;
• Research novel data structures to speed up basic operator execution on traditional CPUs with vector and matrix SIMD, as well as less traditional xPUs such as AI accelerators;
• Ensure solvers may be easily expressed as data-centric C++ control flow around calls to basic operators that automatically dispatch the solver over potentially multiple xPUs;
• Use, and, if necessary, extend run-time systems and communication layers to achieve higher basic operator efficiency, better scalability, and automate computational trade-offs;
• Ensure the quality and performance of all solvers implemented on top of our basic operators, enabling the solution of next-generation scientific and industrial problems.

Successful candidates will have in-depth experience with several of the following
:

• Optimization of irregular algorithms, such as graph computations or sparse numerical linear algebra, touching on all of high-level data structures and algorithms to low-level code optimisations such as SIMD, coarse- and fine-grained locking mechanisms;
• Multi-core, many-core, programming (e.g., POSIX Threads or OpenMP);
• Distributed-memory programming (e.g., MPI, BSP, or LPF), both using collective communications as well as raw RDMA;
• Experience with code generation for high-performance computations and/or in-depth knowledge of their underlying methodologies (e.g., ALP, BLIS, DaCE, Spiral, Flame, Firedrake, et cetera).

• Generic programming in C++11 (or higher), with strong knowledge of standard algorithms and data structures as found in the STL and beyond;
• Performance analysis and parallel debugging (e.g., Valgrind, GNU Debugger, CI testing);
• Excellent written and verbal communication skills with a proven ability to present complex technical information clearly and concisely to a variety of audiences;
• Track record of publications at top HPC or applied math conferences or journals;
• Collaborative work style with the ability to work in a multicultural environment.

• Graph
  
  BLAS or Algebraic Programming (ALP);
• Any aspect of optimization or their key solvers;
• State-of-the-art fabrics and their programming (e.g., Infiniband & ibverbs);
• Publications at top venues in physical sciences or theoretical computer science; and
• SIMT or accelerator programming (e.g., CUDA, OpenCL)