714 research outputs found
Diversification Across Mining Pools: Optimal Mining Strategies under PoW
Mining is a central operation of all proof-of-work (PoW) based
cryptocurrencies. The vast majority of miners today participate in "mining
pools" instead of "solo mining" in order to lower risk and achieve a more
steady income. However, this rise of participation in mining pools negatively
affects the decentralization levels of most cryptocurrencies. In this work, we
look into mining pools from the point of view of a miner: We present an
analytical model and implement a computational tool that allows miners to
optimally distribute their computational power over multiple pools and PoW
cryptocurrencies (i.e. build a mining portfolio), taking into account their
risk aversion levels. Our tool allows miners to maximize their risk-adjusted
earnings by diversifying across multiple mining pools which enhances PoW
decentralization. Finally, we run an experiment in Bitcoin historical data and
demonstrate that a miner diversifying over multiple pools, as instructed by our
model/tool, receives a higher overall Sharpe ratio (i.e. average excess reward
over its standard deviation/volatility).Comment: 13 pages, 16 figures. Presented at WEIS 201
Predictive runtime code scheduling for heterogeneous architectures
Heterogeneous architectures are currently widespread. With
the advent of easy-to-program general purpose GPUs, virtually every re-
cent desktop computer is a heterogeneous system. Combining the CPU
and the GPU brings great amounts of processing power. However, such
architectures are often used in a restricted way for domain-speci c appli-
cations like scienti c applications and games, and they tend to be used
by a single application at a time. We envision future heterogeneous com-
puting systems where all their heterogeneous resources are continuously
utilized by di erent applications with versioned critical parts to be able
to better adapt their behavior and improve execution time, power con-
sumption, response time and other constraints at runtime. Under such a
model, adaptive scheduling becomes a critical component.
In this paper, we propose a novel predictive user-level scheduler based on
past performance history for heterogeneous systems. We developed sev-
eral scheduling policies and present the study of their impact on system
performance. We demonstrate that such scheduler allows multiple appli-
cations to fully utilize all available processing resources in CPU/GPU-
like systems and consistently achieve speedups ranging from 30% to 40%
compared to just using the GPU in a single application mode.Postprint (published version
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