Marat Dukhan

I am Ph.D. student in Computational Science and Engineering at the Georgia Tech's College of Computing. My research interests are in high-performance computing, data analysis, and their interaction.

Publications

Conference Publications

IPDPS 2014, May 19-23, 2014

Jee Choi, Marat Dukhan, Xing Liu, Richard Vuduc "Algorithmic time, energy, and power on candidate HPC compute building blocks"

PyHPC 2013, November 18, 2013

Marat Dukhan "PeachPy: A Python Framework for Developing High-Performance Assembly Kernels"

PPAM 10, September 10, 2013

Marat Dukhan and Richard Vuduc "Methods for high-throughput computation of elementary functions"

SuperComputing 2012, November 10-16, 2012

William B. March, Kenneth Czechowski, Marat Dukhan, Thomas Benson, Dongryeol Lee, Andrew J. Connolly, Richard Vuduc, Edmond Chow, and Alexander G. Gray "Optimizing the Computation of N-Point Correlations on Large-Scale Astronomical Data."

Poster Presentations

Hot Chips 25, August 25–28, 2013

Marat Dukhan "What a Fast FPU Means for Algorithms: A Story of Vector Elementary Functions"

Other Presentations (not peer-reviewed)

BLIS Retreat 2014 workshop, September 25–26, 2013

Marat Dukhan "BLIS for the Web"

Browse Interactive Presentation

The 1st BLIS Retreat workshop, September 5–6, 2013

Marat Dukhan "Developing low-level assembly kernels with Peach-Py"

Teaching

Fall 2012

CSE 6230 - High Performance Computing: Tools and Applications

Assisted Prof. Richard Vuduc and fully covered three weeks of the class

Fall 2013

CSE 6230 - High Performance Computing: Tools and Applications

Assisted Prof. Richard Vuduc and fully covered four weeks of the class

Public Projects

Furious.js JavaScript Library

JavaScript library for hardware-accelerated scientific computing
  • Provides pure JavaScript interface similar to NumPy/SciPy
  • Supports asynchronous computations
  • Offloads computations to Portable Native Client, Web Worker, WebCL or cloud server (via Web Sockets)

PEACH-Py: Portable Efficient Assembly Code-generation in High-level Python

Python Framework for Automating Development of High-Performance Assembly Kernels
  • Supports automatic register allocation
  • Provides stack frame management, including re-aligning of stack frame as needed
  • Generates versions of a function for different calling conventions from the same source (e.g. functions for Microsoft x64 ABI and System V x86-64 ABI can be generated from the same source)
  • Allows to define constants in the place where they are used (just like in high-level languages)
  • Tracks of instruction extensions used in the function.
  • Can multiplex multiple instruction streams (helpful for software pipelining)

Yeppp! High-performance library

Provides a collection of low-level functions optimized for modern processors
  • Library functions have multiple implementations, optimized for different processor architectures
  • The optimal function is chosen at run-time depending on the processor
  • Detects processor microarchitecture and instruction set extensions
  • Provides portable access to CPU cycle counter and high-resolution system timer
  • Available for Windows, Linux, Mac OS X, and Android
  • Supports x86, x86-64, ARM, MIPS, and PowerPC architectures
  • C and C++-compatible header files, and bindings for FORTRAN, Java and .Net/Mono
  • BSD license

Yeppp! CPUID for Android

Shows detailed information about the mobile CPU:
  • CPU architecture (ARM, x86, or MIPS)
  • CPU vendor (e.g. ARM, Qualcomm, Intel, MIPS)
  • CPU microarchitecture (e.g. ARM11, Cortex-A9, Atom, XBurst)
  • Minimum and maximum frequency
  • Number of logical cores
  • Supported instruction set extensions (e.g. NEON, VFPv4, SSSE3, MIPS3D)
  • Size of level-1, level-2, and level-3 caches.

Education

2011 – now Georgia Institute of Technology, College of Computing
Candidate for Ph.D. in Computational Science and Engineering
  • Research advisor: Richard Vuduc
2009 – 2011 New Economic School in Moscow
M.A. in Economics
  • Master's thesis: "Regime Switching Autoregression and Volatility Jumps"
2005 – 2009 Moscow Institute of Physics and Technology
B.Sc. in Applied Mathematics and Physics
  • Bachelor's thesis: "Entropy Coding Optimization for H.264 Video Codec"

Contact Information

E-mail: