Configuring software is a powerful mean to reach functional and performance goals of a system. However, many layers (hardware, operating system, input data, etc), themselves subject to variability, can alter performances of software configurations. For instance, configurations' options of the x264 video encoder may have very different effects on x264's encoding time when used with different input videos, depending on the hardware on which it is executed. In this vision paper, we coin the term deep software variability to refer to the interaction of all external layers modifying the behavior or non-functional properties of a software. Deep software variability challenges practitioners and researchers: the combinatorial explosion of possible executing environments complicates the understanding, the configuration, the maintenance, the debug, and the test of configurable systems. There are also opportunities: harnessing all variability layers (and not only the software layer) can lead to more efficient systems and configuration knowledge that truly generalizes to any usage and context.
SPLC 2021 - The Interplay of Compile-time and Run-time Options for Performan...Luc Lesoil
Many software projects are configurable through compile-time options (e.g. using ./configure) and also through run-time options (e.g. command-line parameters, fed to the software at the execution).
Several works have studied how to predict the effect of run-time options on performance.
However it is unclear how these prediction models generalize when the software is built with different values of compile-time options.
For instance, is the best run-time configuration always the best w.r.t. the chosen compilation options?
In this paper, we investigate the effect of compile-time options on the performance distributions of 5 software systems.
We prove there can exist an interplay between the compile-time and the execution levels, by exhibiting a case where different compilation options significantly alter the performance distributions of a software system.
VaMoS 2022 - Transfer Learning across Distinct Software SystemsLuc Lesoil
Many research studies predict the performance of configurable software using machine learning techniques, thus requiring large amounts of data. Transfer learning aims to reduce the amount of data needed to train these models and has been successfully applied on different executing environments (hardware) or software versions. In this paper we investigate for the first time the idea of applying transfer learning between distinct configurable systems. We design a study involving two video encoders (namely x264 and x265) coming from different code bases. Our results are encouraging since transfer learning outperforms traditional learning for two performance properties (out of three). We discuss the open challenges to overcome for a more general application.
Transfer Learning for Performance Analysis of Configurable Systems: A Causal ...Pooyan Jamshidi
Modern systems (e.g., deep neural networks, big data analytics, and compilers) are highly configurable, which means they expose different performance behavior under different configurations. The fundamental challenge is that one cannot simply measure all configurations due to the sheer size of the configuration space. Transfer learning has been used to reduce the measurement efforts by transferring knowledge about performance behavior of systems across environments. Previously, research has shown that statistical models are indeed transferable across environments. In this work, we investigate identifiability and transportability of causal effects and statistical relations in highly-configurable systems. Our causal analysis agrees with previous exploratory analysis~\cite{Jamshidi17} and confirms that the causal effects of configuration options can be carried over across environments with high confidence. We expect that the ability to carry over causal relations will enable effective performance analysis of highly-configurable systems.
SPLC 2021 - The Interplay of Compile-time and Run-time Options for Performan...Luc Lesoil
Many software projects are configurable through compile-time options (e.g. using ./configure) and also through run-time options (e.g. command-line parameters, fed to the software at the execution).
Several works have studied how to predict the effect of run-time options on performance.
However it is unclear how these prediction models generalize when the software is built with different values of compile-time options.
For instance, is the best run-time configuration always the best w.r.t. the chosen compilation options?
In this paper, we investigate the effect of compile-time options on the performance distributions of 5 software systems.
We prove there can exist an interplay between the compile-time and the execution levels, by exhibiting a case where different compilation options significantly alter the performance distributions of a software system.
VaMoS 2022 - Transfer Learning across Distinct Software SystemsLuc Lesoil
Many research studies predict the performance of configurable software using machine learning techniques, thus requiring large amounts of data. Transfer learning aims to reduce the amount of data needed to train these models and has been successfully applied on different executing environments (hardware) or software versions. In this paper we investigate for the first time the idea of applying transfer learning between distinct configurable systems. We design a study involving two video encoders (namely x264 and x265) coming from different code bases. Our results are encouraging since transfer learning outperforms traditional learning for two performance properties (out of three). We discuss the open challenges to overcome for a more general application.
Transfer Learning for Performance Analysis of Configurable Systems: A Causal ...Pooyan Jamshidi
Modern systems (e.g., deep neural networks, big data analytics, and compilers) are highly configurable, which means they expose different performance behavior under different configurations. The fundamental challenge is that one cannot simply measure all configurations due to the sheer size of the configuration space. Transfer learning has been used to reduce the measurement efforts by transferring knowledge about performance behavior of systems across environments. Previously, research has shown that statistical models are indeed transferable across environments. In this work, we investigate identifiability and transportability of causal effects and statistical relations in highly-configurable systems. Our causal analysis agrees with previous exploratory analysis~\cite{Jamshidi17} and confirms that the causal effects of configuration options can be carried over across environments with high confidence. We expect that the ability to carry over causal relations will enable effective performance analysis of highly-configurable systems.
Koichi Hirai, Fujitsu
Post-K use Arm based super computer. But there are not too many Arm based servers for HPC.
Therefore we think to need to build Arm HPC Ecosystem until Post-K release.
In this presentation, we describe our collaboration efforts to build the Arm HPC Ecosystem.
HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...AMD Developer Central
Presentation HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Matrix Factorization, by Huming Zhu at the AMD Developer Summit (APU13) November 11-13, 2013.
Recorded video here:
http://on-demand.gputechconf.com/siggraph/2017/video/sig1757-tristan-lorach-vkFX-effective-approach-for-vulkan-api.html
Vulkan is a complex low-level API, full of structures and dedicated objects. Using it may be tedious and often leads to complicated source code. We propose here a way to define and use Vulkan components in a convenient and readable way. Then we will show how this infrastructure allows to introduce and use higher concepts, such as Techniques, Passes; and even how to instantiate resources, render-targets right from within the effect, making it self-sufficient and consistent as a general description. The overall purpose of this open-source project is to improve and enhance the use of Vulkan API, while keeping its strength and flexibility. This project can run in two different ways: either as a compiler generating C++ code for you; or at runtime, to load effects and use them right away.
vkFx comes from a former project called nvFx, presented few years ago. While nvFx was intended to be Generic (OpenGL & D3D compliant), vkFx is Vulkan-specific: so the project is thin and doesn’t break important paradigms that Vulkan requires to stay powerful.
Gain a deeper understanding of how to debug and profile your content running with IL2CPP. In depth examples demonstrate how to diagnose problems and improve performance.
Generalized Pipeline Parallelism for DNN TrainingDatabricks
DNN training is extremely time-consuming, necessitating efficient multi-accelerator parallelization. Current approaches to parallelizing training primarily use intra-batch parallelization, where a single iteration of training is split over the available workers, but suffer from diminishing returns at higher worker counts. We present PipeDream, a system that adds inter-batch pipelining to intra-batch parallelism to further improve parallel training throughput, helping to better overlap computation with communication and reduce the amount of communication when possible. Unlike traditional pipelining, DNN training is bi-directional, where a forward pass through the computation graph is followed by a backward pass that uses state and intermediate data computed during the forward pass.
Design of 32 bit Parallel Prefix Adders IOSR Journals
In this paper, we propose 32 bit Kogge-Stone, Brent-Kung, Ladner-Fischer parallel prefix adders. In
general N-bit adders like Ripple Carry Adders (slow adders compare to other adders), and Carry Look Ahead
adders (area consuming adders) are used in earlier days. But now the most Industries are using parallel prefix
adders because of their advantages compare to other adders. Parallel prefix adders are faster and area
efficient. Parallel prefix adder is a technique for increasing the speed in DSP processor while performing
addition. We simulate and synthesis different types of 32-bit prefix adders using Xilinx ISE 10.1i tool. By using
these synthesis results, we noted the performance parameters like number of LUTs and delay. We compare these three adders in terms of LUTs (represents area) and delay values.
Presentation & discussion around low-level graphics APIs. This was a quickly made presentation that I put together for a discussion with Intel and fellow ISVs, thought it could be worth sharing
By: Marianne Eggett, Linux Emerging Technology Practice Mgr, Mainline Information Systems
Are you considering a migration to Linux on IBM System z? The first step is to develop a detailed plan that outlines the short term and long term benefits of your migration.
In this presentation you will learn:
- How to identify the business case to support consolidation with System z Linux
- Examples of cost savings other businesses have experienced
- How to build a Total Cost of Ownership report specific to your environment
To view this presentation with audio, visit: http://go.mainline.com/pages/start/knowledge-center-building-the-case-zlinux-webcast-june-2009/index.html?Campaign_Id=7071&Activity_Id=6131
For other topics, visit: www.mainline.com/kc
Introduction to Software Defined Visualization (SDVis)Intel® Software
Software defined visualization (SDVis) is an open-source initiative from Intel and industry collaborators. Improve the visual fidelity, performance, and efficiency of prominent visualization solutions, while supporting the rapidly growing big data use on workstations through high-performance computing (HPC) on supercomputing clusters without memory limitations and cost of GPU-based solutions.
Koichi Hirai, Fujitsu
Post-K use Arm based super computer. But there are not too many Arm based servers for HPC.
Therefore we think to need to build Arm HPC Ecosystem until Post-K release.
In this presentation, we describe our collaboration efforts to build the Arm HPC Ecosystem.
HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...AMD Developer Central
Presentation HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Matrix Factorization, by Huming Zhu at the AMD Developer Summit (APU13) November 11-13, 2013.
Recorded video here:
http://on-demand.gputechconf.com/siggraph/2017/video/sig1757-tristan-lorach-vkFX-effective-approach-for-vulkan-api.html
Vulkan is a complex low-level API, full of structures and dedicated objects. Using it may be tedious and often leads to complicated source code. We propose here a way to define and use Vulkan components in a convenient and readable way. Then we will show how this infrastructure allows to introduce and use higher concepts, such as Techniques, Passes; and even how to instantiate resources, render-targets right from within the effect, making it self-sufficient and consistent as a general description. The overall purpose of this open-source project is to improve and enhance the use of Vulkan API, while keeping its strength and flexibility. This project can run in two different ways: either as a compiler generating C++ code for you; or at runtime, to load effects and use them right away.
vkFx comes from a former project called nvFx, presented few years ago. While nvFx was intended to be Generic (OpenGL & D3D compliant), vkFx is Vulkan-specific: so the project is thin and doesn’t break important paradigms that Vulkan requires to stay powerful.
Gain a deeper understanding of how to debug and profile your content running with IL2CPP. In depth examples demonstrate how to diagnose problems and improve performance.
Generalized Pipeline Parallelism for DNN TrainingDatabricks
DNN training is extremely time-consuming, necessitating efficient multi-accelerator parallelization. Current approaches to parallelizing training primarily use intra-batch parallelization, where a single iteration of training is split over the available workers, but suffer from diminishing returns at higher worker counts. We present PipeDream, a system that adds inter-batch pipelining to intra-batch parallelism to further improve parallel training throughput, helping to better overlap computation with communication and reduce the amount of communication when possible. Unlike traditional pipelining, DNN training is bi-directional, where a forward pass through the computation graph is followed by a backward pass that uses state and intermediate data computed during the forward pass.
Design of 32 bit Parallel Prefix Adders IOSR Journals
In this paper, we propose 32 bit Kogge-Stone, Brent-Kung, Ladner-Fischer parallel prefix adders. In
general N-bit adders like Ripple Carry Adders (slow adders compare to other adders), and Carry Look Ahead
adders (area consuming adders) are used in earlier days. But now the most Industries are using parallel prefix
adders because of their advantages compare to other adders. Parallel prefix adders are faster and area
efficient. Parallel prefix adder is a technique for increasing the speed in DSP processor while performing
addition. We simulate and synthesis different types of 32-bit prefix adders using Xilinx ISE 10.1i tool. By using
these synthesis results, we noted the performance parameters like number of LUTs and delay. We compare these three adders in terms of LUTs (represents area) and delay values.
Presentation & discussion around low-level graphics APIs. This was a quickly made presentation that I put together for a discussion with Intel and fellow ISVs, thought it could be worth sharing
By: Marianne Eggett, Linux Emerging Technology Practice Mgr, Mainline Information Systems
Are you considering a migration to Linux on IBM System z? The first step is to develop a detailed plan that outlines the short term and long term benefits of your migration.
In this presentation you will learn:
- How to identify the business case to support consolidation with System z Linux
- Examples of cost savings other businesses have experienced
- How to build a Total Cost of Ownership report specific to your environment
To view this presentation with audio, visit: http://go.mainline.com/pages/start/knowledge-center-building-the-case-zlinux-webcast-june-2009/index.html?Campaign_Id=7071&Activity_Id=6131
For other topics, visit: www.mainline.com/kc
Introduction to Software Defined Visualization (SDVis)Intel® Software
Software defined visualization (SDVis) is an open-source initiative from Intel and industry collaborators. Improve the visual fidelity, performance, and efficiency of prominent visualization solutions, while supporting the rapidly growing big data use on workstations through high-performance computing (HPC) on supercomputing clusters without memory limitations and cost of GPU-based solutions.
Keynote VariVolution/VM4ModernTech@SPLC 2022
At compile-time or at runtime, varying software is a powerful means to achieve optimal functional and performance goals. An observation is that only considering the software layer might be naive to tune the performance of the system or test that the functionality behaves correctly. In fact, many layers (hardware, operating system, input data, build process etc.), themselves subject to variability, can alter performances of software configurations. For instance, configurations' options may have very different effects on execution time or energy consumption when used with different input data, depending on the way it has been compiled and the hardware on which it is executed.
In this talk, I will introduce the concept of “deep software variability” which refers to the interactions of all external layers modifying the behavior or non-functional properties of a software system. I will show how compile-time options, inputs, and software evolution (versions), some dimensions of deep variability, can question the generalization of the variability knowledge of popular configurable systems like Linux, gcc, xz, or x264.
I will then argue that machine learning (ML) is particularly suited to manage very large variants space. The key idea of ML is to build a model based on sample data -- here observations about software variants in variable settings -- in order to make predictions or decisions. I will review state-of-the-art solutions developed in software engineering and software product line engineering while connecting with works in ML (e.g., transfer learning, dimensionality reduction, adversarial learning). Overall, the key challenge is to leverage the right ML pipeline in order to harness all variability layers (and not only the software layer), leading to more efficient systems and variability knowledge that truly generalizes to any usage and context.
From this perspective, we are starting an initiative to collect data, software, reusable artefacts, and body of knowledge related to (deep) software variability: https://deep.variability.io
Finally, I will open a broader discussion on how machine learning and deep software variability relate to the reproducibility, replicability, and robustness of scientific, software-based studies (e.g., in neuroimaging and climate modelling).
Recover 30% of your day with IBM Development Tools (Smarter Mainframe Develop...Susan Yoskin
If you need to attract new developers, and want to keep your company’s name out of the headlines, then this session is for you. When your business depends on your mainframe apps working and performing well—all the time—you need to be alerted to issues as they occur and have the tools to help you find and fix the problems and test your solutions before disaster strikes (we’ve all been in those late night and weekend drills). You also need to continue supporting these applications for years to come, and that will require new talent.
This session will introduce you to the development environments that college grads are already comfortable with, and help your applications become more resilient at the same time. We’ll walk you through the tools to help you accomplish all of this and demo some scenarios to show you how efficiently our tools can perform the tasks that slow you down.
(Costless) Software Abstractions for Parallel ArchitecturesJoel Falcou
Performing large, intensive or non-trivial computing on array like data structures is one of the most common task in scientific computing, video game development and other fields. This matter of fact is backed up by the large number of tools, languages and libraries to perform such tasks. If we restrict ourselves to C++ based solutions, more than a dozen such libraries exists from BLAS/LAPACK C++ binding to template meta-programming based Blitz++ or Eigen. If all of these libraries provide good performance or good abstraction, none of them seems to fit the need of so many different user types.
Moreover, as parallel system complexity grows, the need to maintain all those components quickly become unwieldy. This talk explores various software design techniques - like Generative Programming, MetaProgramming and Generic Programming - and their application to the implementation of a parallel computing librariy in such a way that:
- abstraction and expressiveness are maximized - cost over efficiency is minimized
We'll skim over various applications and see how they can benefit from such tools. We will conclude by discussing what lessons were learnt from this kind of implementation and how those lessons can translate into new directions for the language itself.
Qualcomm® Snapdragon™ processors, a product of Qualcomm Technologies, Inc., boast a long list of technologies, from the CPU and GPU, to audio, video, display, networking and much more. In this session, you’ll learn how to take advantage of these features and technologies to create the best gaming experiences, including all the available tools.
Watch this presentation on YouTube: https://www.youtube.com/watch?v=NhbZK_5na7U&list=PLxeazpXYyqtNm2EnCbfSzy7aKOkHjiaSi&index=31
Learn more about developing mobile apps for devices powered by Snapdragon processors: https://developer.qualcomm.com/mobile-development/maximize-hardware/mobile-gaming-graphics-adreno
A presentation I made at OpenStack Summit in Paris (November 2014) showing the Remote Rendering plateform built in the XLCloud project. The main topic of the presentation is related to optimizing the video encoding by analysing the images and user attention.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/seamless-deployment-of-multimedia-and-machine-learning-applications-at-the-edge-a-presentation-from-qualcomm/
Megha Daga, Senior Director of Product Management for AIoT at Qualcomm, presents the “Seamless Deployment of Multimedia and Machine Learning Applications at the Edge” tutorial at the May 2022 Embedded Vision Summit.
There has been an explosion of opportunities for edge compute solutions across the internet of things. This growth in opportunities and the diversity of applications is leading to fragmentation in the IoT space both in hardware and software, which creates challenges for developers. In addition, customers and developers are facing challenges in efficient data management and optimized application deployment on embedded edge platforms.
In this session, Daga introduces the Qualcomm Intelligent Multimedia SDK, which empowers developers to tackle these challenges and deploy edge compute applications in a scalable, flexible and optimized way. The Qualcomm Intelligent Multimedia SDK easily decodes and organizes sensor data and executes applications efficiently on edge platforms.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
9. What is Deep Software Variability ? 9
The influence of software environment on performances,
the huge number of possible environments,
and the interactions of variability layers
that can alter performances of configurable systems.
11. Age # Cores GPU
Variant
Compil. Version
Version Option Distrib.
Size Length Res.
Hardware
Operating
System
Software
Input Data
Identify the influential layers
Problem
≠ layers,
≠ importances on
performances
Challenge
Estimate their effects
Opportunity
Leverage the useful
variability layers
& variables
11
12. Test & Benchmark environments
0.152.2854 0.155.2917
Problem
Combinatorial explosion
Challenge
Build a
representative
set of environments
Opportunity
Profile environments
12
Hardware
Operating
System
Software
Input Data
13. Transfer performances across environments
10.4
20.04
Dell latitude
7400
Raspberry Pi
4 model B
A B
?
Problem
Options’ importances
change with environments
Challenge
Transfer performances
across environments
Opportunity
Reduce cost of measure
13
14. Cross-Layer Tuning
Age # Cores GPU
Variant
Compil. Version
Version Option Distrib.
Size Length Res.
Hardware
Operating
System
Software
Input Data
14
Problem
(Negative) interactions
of layers
Challenge
Find & fix values to improve
performances
Opportunity
Specialize the environment
for a use case
Bug
Perf. ↗
Perf. ↘
15. How to handle Deep Software Variability ?
Identify the influential layers
Test & Benchmark environments
Transfer performances across environments
Cross-Layer Tuning
15
17. Thank you for your attention !
Luc Lesoil
Mathieu Acher
Arnaud Blouin
Jean-Marc Jézéquel
luc.lesoil@irisa.fr
mathieu.acher@irisa.fr
arnaud.blouin@irisa.fr
jean-marc.jezequel@irisa.fr
✘
✘ If you want to discuss this topic or to work with us :
17
