IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
Building a Big Data Machine Learning PlatformCliff Click
H2O - It's open source, in-memory, big data, clustered computing - Math At Scale. We got the Worlds Fastest Logistic Regression (by a lot!), world's first (and fastest) distributed Gradient Boosted Method (GBM), plus Random Forest, PCA, KMeans++, etc... R's "plyr" style data munging at-scale, including ddply (Group-By for you SQL'rs) and much of R's expressive coding style.
We built H2O, an open-source platform for working with in-memory distributed data. Then we built on top of H2O state-of-the-art predictive modeling and analytics (e.g. GLM & Logistic Regression, GBM, Random Forest, Neural Nets, PCA to name a few) that's 1000x faster than the disk-bound alternatives, and 100x faster than R (we love R but it's tooo slow on big data!). We can run R expressions on tera-scale datasets, or munge data from Scala & Python. We're building our newest algorithms in a few weeks, start to finish, because the platform makes Big Math easy. We routinely test on 100G datasets, have customers using 1T datasets.
This talk is about the platform, coding style & API that lets us seamlessly deal with datasets from 1K to 1TB without changing a line of code, lets us use clusters ranging from your laptop to 100 server clusters with many many TB of ram and hundreds of CPUs.
GraalVM is a virtual machine that can run many languages on top of the Java Virtual Machine. It comes with support for JavaScript, Ruby, Python… But what if you're building a DSL, or your language is not listed? Fear not!
In this session we'll discover what it takes to run another language in GraalVM. Using GraalVM, we don't only get a fast runtime, but we'll also get great tool support. With Brainfuck as an example, we'll see how we can run guest languages inside Java applications. It might not bring us profit, but at least it will bring some fun.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
Building a Big Data Machine Learning PlatformCliff Click
H2O - It's open source, in-memory, big data, clustered computing - Math At Scale. We got the Worlds Fastest Logistic Regression (by a lot!), world's first (and fastest) distributed Gradient Boosted Method (GBM), plus Random Forest, PCA, KMeans++, etc... R's "plyr" style data munging at-scale, including ddply (Group-By for you SQL'rs) and much of R's expressive coding style.
We built H2O, an open-source platform for working with in-memory distributed data. Then we built on top of H2O state-of-the-art predictive modeling and analytics (e.g. GLM & Logistic Regression, GBM, Random Forest, Neural Nets, PCA to name a few) that's 1000x faster than the disk-bound alternatives, and 100x faster than R (we love R but it's tooo slow on big data!). We can run R expressions on tera-scale datasets, or munge data from Scala & Python. We're building our newest algorithms in a few weeks, start to finish, because the platform makes Big Math easy. We routinely test on 100G datasets, have customers using 1T datasets.
This talk is about the platform, coding style & API that lets us seamlessly deal with datasets from 1K to 1TB without changing a line of code, lets us use clusters ranging from your laptop to 100 server clusters with many many TB of ram and hundreds of CPUs.
GraalVM is a virtual machine that can run many languages on top of the Java Virtual Machine. It comes with support for JavaScript, Ruby, Python… But what if you're building a DSL, or your language is not listed? Fear not!
In this session we'll discover what it takes to run another language in GraalVM. Using GraalVM, we don't only get a fast runtime, but we'll also get great tool support. With Brainfuck as an example, we'll see how we can run guest languages inside Java applications. It might not bring us profit, but at least it will bring some fun.
Building a DSL with GraalVM (VoxxedDays Luxembourg)Maarten Mulders
GraalVM is a virtual machine that can run many languages on top of the Java Virtual Machine. It comes with support for JavaScript, Ruby, Python… But what if you're building a DSL, or your language is not listed? Fear not!
In this session we'll discover what it takes to run another language in GraalVM. Using GraalVM, we don't only get a fast runtime, but we'll also get great tool support. With Brainfuck as an example, we'll see how we can run guest languages inside Java applications. It might not bring us profit, but at least it will bring some fun.
Nowadays, scaling and auto-scaling have become relatively easy tasks. Everyone knows how to set up auto-scaling environments - Auto-Scaling groups, Swarm, Kubernetes, etc.
But when we try to scale I/O Bound workloads:
- Message queues (Kafka, Rabbit, NATS)
- Distributed databases (Hadoop, Cassandra)
- Storage subsystems (CEPH, GlusterFS, HDFS),
the traditional auto-scaling mechanisms are just not enough.
Heavy calculations must be performed to determine the I/O bottlenecks. Rebalancing the data after a scaling event can take up to hours depending on your data & could, resulting in data loss if not properly designed.
We will deep dive into this type of workload and walk you through code samples you can apply in your own environment.
Analysis of Haiku Operating System (BeOS Family) by PVS-Studio. Part 2PVS-Studio
This is the second and last part of the large article about analysis of the Haiku operating system. In the first article, we discussed a variety of possible errors all of which one way or another deal with conditions. In this article, we will discuss the remaining analyzer warnings I have selected for you. The bug examples are grouped into several categories.
SFO15-500: VIXL
Speaker: Amaury Le Leyzour
Date: September 25, 2015
★ Session Description ★
VIXL is dynamic code generation toolkit for ARMv8 that we hope will enable JIT creators to rapidly target the ARM instruction set.
Over the past few years we (the ARM JIT team) have worked on the code generators of many of the leading JIT compilers for the JavaScript and Java languages. During that time we built up a strong knowledge base on some of the pitfalls and time-sinks involved in creating a good JIT compiler backend. This led us to develop some tools to help improve our productivity. With ARM announcing the new Cortex-A range of processors supporting the AArch64 execution state we decided that we would focus our efforts on A64 tooling to enable developers to rapidly port programming language virtual machines for this new processor range. Soon after we decided to support Aarch32 as well.
This presentation will introduce you to what VIXL is, what’s new in VIXL and how to use it and take advantage of all its components that cover all the aspects of software development on ARM CPUs.
★ Resources ★
Video: https://www.youtube.com/watch?v=XxMTSO4clQY
Etherpad: pad.linaro.org/p/sfo15-500
Pathable: https://sfo15.pathable.com/meetings/303091
★ Event Details ★
Linaro Connect San Francisco 2015 - #SFO15
September 21-25, 2015
Hyatt Regency Hotel
http://www.linaro.org
http://connect.linaro.org
Since the emerging of the OpenStack cloud computing platform in the Ubuntu community, increasing number of public/private cloud service providers choose to deploy it all over the world. Recently, Spectre and Meltdown have caused a panic in the world and the Spectre V2 is the only one which can attack the host system from the guest VM. It's vital to know the detailed process of the attack. Gavin Guo will give a detail explanation and an example of how to attack the host system. Besides, v1/v3/v4 are also introduced in the slide.
Exploring Compiler Optimization Opportunities for the OpenMP 4.x Accelerator...Akihiro Hayashi
Third Workshop on Accelerator Programming Using Directives (WACCPD2016, co-located with SC16)
While GPUs are increasingly popular for high-performance
computing, optimizing the performance of GPU programs is a time-consuming and non-trivial process in general. This complexity stems from the low abstraction level of standard
GPU programming models such as CUDA and OpenCL:
programmers are required to orchestrate low-level operations
in order to exploit the full capability of GPUs. In terms of
software productivity and portability, a more attractive approach
would be to facilitate GPU programming by providing high-level
abstractions for expressing parallel algorithms.
OpenMP is a directive-based shared memory parallel programming model and has been widely used for many years.
From OpenMP 4.0 onwards, GPU platforms are supported
by extending OpenMP’s high-level parallel abstractions with
accelerator programming. This extension allows programmers to
write GPU programs in standard C/C++ or Fortran languages,
without exposing too many details of GPU architectures.
However, such high-level parallel programming strategies generally impose additional program optimizations on compilers,
which could result in lower performance than fully hand-tuned
code with low-level programming models.To study potential
performance improvements by compiling and optimizing high-level GPU programs, in this paper, we 1) evaluate a set of
OpenMP 4.x benchmarks on an IBM POWER8 and NVIDIA
Tesla GPU platform and 2) conduct a comparable performance
analysis among hand-written CUDA and automatically-generated
GPU programs by the IBM XL and clang/LLVM compilers.
Hadoop World 2011: Leveraging Hadoop for Legacy Systems - Mathias Herberts, C...Cloudera, Inc.
Since many companies in the financial sector still rely on legacy systems for its daily operations, Hadoop can only be truly useful in those environments if it can fit nicely among COBOL, VSAM, MVS and other legacy technologies. In this session, we will detail how Crédit Mutuel Arkéa solved this challenge and successfully mixed the mainframe and Hadoop.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
Building a DSL with GraalVM (VoxxedDays Luxembourg)Maarten Mulders
GraalVM is a virtual machine that can run many languages on top of the Java Virtual Machine. It comes with support for JavaScript, Ruby, Python… But what if you're building a DSL, or your language is not listed? Fear not!
In this session we'll discover what it takes to run another language in GraalVM. Using GraalVM, we don't only get a fast runtime, but we'll also get great tool support. With Brainfuck as an example, we'll see how we can run guest languages inside Java applications. It might not bring us profit, but at least it will bring some fun.
Nowadays, scaling and auto-scaling have become relatively easy tasks. Everyone knows how to set up auto-scaling environments - Auto-Scaling groups, Swarm, Kubernetes, etc.
But when we try to scale I/O Bound workloads:
- Message queues (Kafka, Rabbit, NATS)
- Distributed databases (Hadoop, Cassandra)
- Storage subsystems (CEPH, GlusterFS, HDFS),
the traditional auto-scaling mechanisms are just not enough.
Heavy calculations must be performed to determine the I/O bottlenecks. Rebalancing the data after a scaling event can take up to hours depending on your data & could, resulting in data loss if not properly designed.
We will deep dive into this type of workload and walk you through code samples you can apply in your own environment.
Analysis of Haiku Operating System (BeOS Family) by PVS-Studio. Part 2PVS-Studio
This is the second and last part of the large article about analysis of the Haiku operating system. In the first article, we discussed a variety of possible errors all of which one way or another deal with conditions. In this article, we will discuss the remaining analyzer warnings I have selected for you. The bug examples are grouped into several categories.
SFO15-500: VIXL
Speaker: Amaury Le Leyzour
Date: September 25, 2015
★ Session Description ★
VIXL is dynamic code generation toolkit for ARMv8 that we hope will enable JIT creators to rapidly target the ARM instruction set.
Over the past few years we (the ARM JIT team) have worked on the code generators of many of the leading JIT compilers for the JavaScript and Java languages. During that time we built up a strong knowledge base on some of the pitfalls and time-sinks involved in creating a good JIT compiler backend. This led us to develop some tools to help improve our productivity. With ARM announcing the new Cortex-A range of processors supporting the AArch64 execution state we decided that we would focus our efforts on A64 tooling to enable developers to rapidly port programming language virtual machines for this new processor range. Soon after we decided to support Aarch32 as well.
This presentation will introduce you to what VIXL is, what’s new in VIXL and how to use it and take advantage of all its components that cover all the aspects of software development on ARM CPUs.
★ Resources ★
Video: https://www.youtube.com/watch?v=XxMTSO4clQY
Etherpad: pad.linaro.org/p/sfo15-500
Pathable: https://sfo15.pathable.com/meetings/303091
★ Event Details ★
Linaro Connect San Francisco 2015 - #SFO15
September 21-25, 2015
Hyatt Regency Hotel
http://www.linaro.org
http://connect.linaro.org
Since the emerging of the OpenStack cloud computing platform in the Ubuntu community, increasing number of public/private cloud service providers choose to deploy it all over the world. Recently, Spectre and Meltdown have caused a panic in the world and the Spectre V2 is the only one which can attack the host system from the guest VM. It's vital to know the detailed process of the attack. Gavin Guo will give a detail explanation and an example of how to attack the host system. Besides, v1/v3/v4 are also introduced in the slide.
Exploring Compiler Optimization Opportunities for the OpenMP 4.x Accelerator...Akihiro Hayashi
Third Workshop on Accelerator Programming Using Directives (WACCPD2016, co-located with SC16)
While GPUs are increasingly popular for high-performance
computing, optimizing the performance of GPU programs is a time-consuming and non-trivial process in general. This complexity stems from the low abstraction level of standard
GPU programming models such as CUDA and OpenCL:
programmers are required to orchestrate low-level operations
in order to exploit the full capability of GPUs. In terms of
software productivity and portability, a more attractive approach
would be to facilitate GPU programming by providing high-level
abstractions for expressing parallel algorithms.
OpenMP is a directive-based shared memory parallel programming model and has been widely used for many years.
From OpenMP 4.0 onwards, GPU platforms are supported
by extending OpenMP’s high-level parallel abstractions with
accelerator programming. This extension allows programmers to
write GPU programs in standard C/C++ or Fortran languages,
without exposing too many details of GPU architectures.
However, such high-level parallel programming strategies generally impose additional program optimizations on compilers,
which could result in lower performance than fully hand-tuned
code with low-level programming models.To study potential
performance improvements by compiling and optimizing high-level GPU programs, in this paper, we 1) evaluate a set of
OpenMP 4.x benchmarks on an IBM POWER8 and NVIDIA
Tesla GPU platform and 2) conduct a comparable performance
analysis among hand-written CUDA and automatically-generated
GPU programs by the IBM XL and clang/LLVM compilers.
Hadoop World 2011: Leveraging Hadoop for Legacy Systems - Mathias Herberts, C...Cloudera, Inc.
Since many companies in the financial sector still rely on legacy systems for its daily operations, Hadoop can only be truly useful in those environments if it can fit nicely among COBOL, VSAM, MVS and other legacy technologies. In this session, we will detail how Crédit Mutuel Arkéa solved this challenge and successfully mixed the mainframe and Hadoop.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
IMAX3: Amazing Dataflow-Centric CGRA and its Applications
I present this slide to all hungry engineers who are tired of CPU, GPU, FPGA, tensor core, AI core, who want some challenging one with no black box inside, and who want to improve by themselves.
Google Calendar is a versatile tool that allows users to manage their schedules and events effectively. With Google Calendar, you can create and organize calendars, set reminders for important events, and share your calendars with others. It also provides features like creating events, inviting attendees, and accessing your calendar from mobile devices. Additionally, Google Calendar allows you to embed calendars in websites or platforms like SlideShare, making it easier for others to view and interact with your schedules.
Building a Raspberry Pi Robot with Dot NET 8, Blazor and SignalR - Slides Onl...Peter Gallagher
In this session delivered at Leeds IoT, I talk about how you can control a 3D printed Robot Arm with a Raspberry Pi, .NET 8, Blazor and SignalR.
I also show how you can use a Unity app on an Meta Quest 3 to control the arm VR too.
You can find the GitHub repo and workshop instructions here;
https://bit.ly/dotnetrobotgithub