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pro vyhledávání: '"Moses, William S."'
To take full advantage of a specific hardware target, performance engineers need to gain control on compilers in order to leverage their domain knowledge about the program and hardware. Yet, modern compilers are poorly controlled, usually by configur
Externí odkaz:
http://arxiv.org/abs/2409.03864
The size and complexity of software applications is increasing at an accelerating pace. Source code repositories (along with their dependencies) require vast amounts of labor to keep them tested, maintained, and up to date. As the discipline now begi
Externí odkaz:
http://arxiv.org/abs/2406.08843
Autor:
Moses, William S.
The decline of Moore’s law and an increasing reliance on computation has led to an explosion of specialized software packages and hardware architectures. While this diversity enables unprecedented flexibility, it also requires domain-experts to lea
Externí odkaz:
https://hdl.handle.net/1721.1/151243
Quantum computing promises transformational gains for solving some problems, but little to none for others. For anyone hoping to use quantum computers now or in the future, it is important to know which problems will benefit. In this paper, we introd
Externí odkaz:
http://arxiv.org/abs/2310.15505
Autor:
Moses, William S., Ivanov, Ivan R., Domke, Jens, Endo, Toshio, Doerfert, Johannes, Zinenko, Oleksandr
While parallelism remains the main source of performance, architectural implementations and programming models change with each new hardware generation, often leading to costly application re-engineering. Most tools for performance portability requir
Externí odkaz:
http://arxiv.org/abs/2207.00257
Autor:
Moses, William S., Churavy, Valentin
Applying differentiable programming techniques and machine learning algorithms to foreign programs requires developers to either rewrite their code in a machine learning framework, or otherwise provide derivatives of the foreign code. This paper pres
Externí odkaz:
http://arxiv.org/abs/2010.01709
Autor:
Shavit, Yonadav, Moses, William S.
An algorithmic decision-maker incentivizes people to act in certain ways to receive better decisions. These incentives can dramatically influence subjects' behaviors and lives, and it is important that both decision-makers and decision-recipients hav
Externí odkaz:
http://arxiv.org/abs/1910.05664
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and
Externí odkaz:
http://hdl.handle.net/1721.1/113124
Autor:
Vasilache, Nicolas, Zinenko, Oleksandr, Theodoridis, Theodoros, Goyal, Priya, DeVito, Zachary, Moses, William S., Verdoolaege, Sven, Adams, Andrew, Cohen, Albert
Deep learning models with convolutional and recurrent networks are now ubiquitous and analyze massive amounts of audio, image, video, text and graph data, with applications in automatic translation, speech-to-text, scene understanding, ranking user p
Externí odkaz:
http://arxiv.org/abs/1802.04730
Autor:
Moses, William S., Demaine, Erik D.
This paper proves that arrangement of music is NP-hard when subject to various constraints: avoiding musical dissonance, limiting how many notes can be played simultaneously, and limiting transition speed between chords. These results imply the compu
Externí odkaz:
http://arxiv.org/abs/1607.04220