5 research outputs found
Development of a Device for Remote Monitoring of Heart Rate and Body Temperature
We present a new integrated, portable device to provide a convenient solution
for remote monitoring heart rate at the fingertip and body temperature using
Ethernet technology and widely spreading internet. Now a days, heart related
disease is rising. Most of the times in these cases, patients may not realize
their actual conditions and even it is a common fact that there are no doctors
by their side, especially in rural areas, but now a days most of the diseases
are curable if detected in time.
We have tried to make a system which may give information about one's
physical condition and help him or her to detect these deadly but curable
diseases. The system gives information of heart rate and body temperature
simultaneously acquired on the portable side in real time and transmits results
to web. In this system, the condition of heart and body temperature can be
monitored from remote places. Eventually, this device provides a low cost,
easily accessible human health monitor solution bridging the gaps between
patients and doctors
LLVM Static Analysis for Program Characterization and Memory Reuse Profile Estimation
Profiling various application characteristics, including the number of
different arithmetic operations performed, memory footprint, etc., dynamically
is time- and space-consuming. On the other hand, static analysis methods,
although fast, can be less accurate. This paper presents an LLVM-based
probabilistic static analysis method that accurately predicts different program
characteristics and estimates the reuse distance profile of a program by
analyzing the LLVM IR file in constant time, regardless of program input size.
We generate the basic-block-level control flow graph of the target application
kernel and determine basic-block execution counts by solving the linear balance
equation involving the adjacent basic blocks' transition probabilities.
Finally, we represent the kernel memory accesses in a bracketed format and
employ a recursive algorithm to calculate the reuse distance profile. The
results show that our approach can predict application characteristics
accurately compared to another LLVM-based dynamic code analysis tool, Byfl.Comment: This paper was accepted at the MEMSYS '23 conference, The
International Symposium on Memory Systems, October 02, 2023 - October 05,
2023, Alexandria, V
BB-ML: Basic Block Performance Prediction using Machine Learning Techniques
Recent years have seen the adoption of Machine Learning (ML) techniques to
predict the performance of large-scale applications, mostly at a coarse level.
In contrast, we propose to use ML techniques for performance prediction at a
much finer granularity, namely at the Basic Block (BB) level, which are single
entry, single exit code blocks that are used for analysis by the compilers to
break down a large code into manageable pieces. We extrapolate the basic block
execution counts of GPU applications and use them for predicting the
performance for large input sizes from the counts of smaller input sizes. We
train a Poisson Neural Network (PNN) model using random input values as well as
the lowest input values of the application to learn the relationship between
inputs and basic block counts. Experimental results show that the model can
accurately predict the basic block execution counts of 16 GPU benchmarks. We
achieve an accuracy of 93.5% in extrapolating the basic block counts for large
input sets when trained on smaller input sets and an accuracy of 97.7% in
predicting basic block counts on random instances. In a case study, we apply
the ML model to CUDA GPU benchmarks for performance prediction across a
spectrum of applications. We use a variety of metrics for evaluation, including
global memory requests and the active cycles of tensor cores, ALU, and FMA
units. Results demonstrate the model's capability of predicting the performance
of large datasets with an average error rate of 0.85% and 0.17% for global and
shared memory requests, respectively. Additionally, to address the utilization
of the main functional units in Ampere architecture GPUs, we calculate the
active cycles for tensor cores, ALU, FMA, and FP64 units and achieve an average
error of 2.3% and 10.66% for ALU and FMA units while the maximum observed error
across all tested applications and units reaches 18.5%.Comment: Accepted at the 29th IEEE International Conference on Parallel and
Distributed Systems (ICPADS 2023
Interfacial and Aggregation Behavior of Dicarboxylic Amino Acid-Based Surfactants in Combination with a Cationic Surfactant
The interfacial and micellization behavior of three dicarboxylic amino acid-based anionic surfactants, abbreviated as AAS (N-dodecyl derivative of -aminomalonate, -aspartate, and -glutamate) in combination with hexadecyltrimethylammonium bromide (HTAB) were investigated by surface tension, conductance, UV-vis absorption/emission spectroscopy, dynamic light scattering (DLS), and viscosity studies. Critical micelle concentration (CMC) values of the surfactant mixtures are significantly lower than the predicted values, indicating associative interaction between the components. Surface excess, limiting molecular area, surface pressure at the CMC, and Gibbs free energy indicate spontaneity of the micellization processes compared to the pure components. CMC values were also determined from the sigmoidal variation in the plot of micellar polarity and pyrene UV vis absorption/emission intensities with surfactant concentration. The aggregation number, determined by static fluorescence quenching method, increases with decreasing mole fraction of the AAS (alpha(AAS)), where the micelles are mainly dominated by the HTAB molecules. The size of the micelle increases with decreasing alpha(AAs), leading to the formation of larger and complex aggregates, as also supported by the viscosity studies. Micelles comprising 20-40 mol % AAS are highly viscous, in consonance with their sizes. Some of the mixed surfactant systems show unusual viscosity (shear thickening and increased viscosity with increasing temperature). Such mixed surfactant systems are considered to have potential in gel-based drug delivery and nanoparticle synthesis