With the diversity of platforms, it is impossible for MPI libraries to automatically provide the best performance for all existing applications. In this session, we demonstrate that Intel® MPI Library is not a black box and contains several features allowing users to enhance MPI applications. From basic (process mapping, collective tuning) to advanced features (unreliable datagram, kernel-assisted approaches), this session covers a large spectrum of possibilities offered by the Intel MPI Library to improve the performance of parallel applications on high-performance computing (HPC) systems.
This session introduces tuning flags and explains features available on Intel MPI Library by highlighting results obtained on a Stampede* cluster. It is designed to help beginner and intermediate Intel MPI Library users to better understand all of the library's capabilities.
Resources
Next Generation MPICH: What to Expect - Lightweight Communication and MoreIntel® Software
MPICH is a widely used, open-source implementation of the message passing interface (MPI) standard. It has been ported to many platforms and used by several vendors and research groups as the basis for their own MPI implementations. This session discusses the current development activity with MPICH, including a close collaboration with teams at Intel. We showcase preparing MPICH-derived implementations for deployment on upcoming supercomputers like Aurora (from the Argonne Leadership Computing Facility), which is based on the Intel® Xeon Phi™ processor and Intel® Omni-Path Architecture (Intel® OPA).
Great Paper on HSAemu Full system simulator built form PQUEMU to do Full System Emulation of HSA from our Academic Member Yeh-Ching Chung of National Tsing Hua University
PEARC17: Interactive Code Adaptation Tool for Modernizing Applications for In...Ritu Arora
Often, HPC software outlives the HPC systems for which they are initially developed. The innovations in the HPC platforms’ hardware and parallel programming standards drive the modernization of HPC applications so that they continue being performant. While such code modernization efforts may not be very challenging for HPC experts and well-funded research groups, many domain-experts may find it challenging to adapt their applications for latest HPC platforms due to lack of expertise, time, and funds. The challenges of such domain-experts can be mitigated by providing them high-level tools for code modernization and migration.
Exploiting Linux Control Groups for Effective Run-time Resource ManagementPatrick Bellasi
Emerging multi/many-core architectures, targeting both High Performance Computing (HPC) and mobile devices,
increase the interest for self-adaptive systems, where both applications and computational resources could smoothly adapt
to the changing of the working conditions. In these scenarios, an efficient Run-Time Resource Manager (RTRM) framework
can provide a valuable support to identify the optimal trade-off between the Quality-of-Service (QoS) requirements of the
applications and the time varying resources availability.
This presentation introduces a new approach to the development of a system-wide RTRM featuring: a) a hierarchical and distributed control, b) the exploitation of design-time information, c) a rich multi-objective optimization strategy and d) a portable and modular design based on a set of tunable policies. The framework is already available as an Open Source project, targeting a NUMA architecture and a new generation multi/many-core research platform. First tests show benefits for the execution of parallel applications, the scalability of the proposed multi-objective resources partitioning strategy, and the sustainability of the overheads introduced by the framework.
With the diversity of platforms, it is impossible for MPI libraries to automatically provide the best performance for all existing applications. In this session, we demonstrate that Intel® MPI Library is not a black box and contains several features allowing users to enhance MPI applications. From basic (process mapping, collective tuning) to advanced features (unreliable datagram, kernel-assisted approaches), this session covers a large spectrum of possibilities offered by the Intel MPI Library to improve the performance of parallel applications on high-performance computing (HPC) systems.
This session introduces tuning flags and explains features available on Intel MPI Library by highlighting results obtained on a Stampede* cluster. It is designed to help beginner and intermediate Intel MPI Library users to better understand all of the library's capabilities.
Resources
Next Generation MPICH: What to Expect - Lightweight Communication and MoreIntel® Software
MPICH is a widely used, open-source implementation of the message passing interface (MPI) standard. It has been ported to many platforms and used by several vendors and research groups as the basis for their own MPI implementations. This session discusses the current development activity with MPICH, including a close collaboration with teams at Intel. We showcase preparing MPICH-derived implementations for deployment on upcoming supercomputers like Aurora (from the Argonne Leadership Computing Facility), which is based on the Intel® Xeon Phi™ processor and Intel® Omni-Path Architecture (Intel® OPA).
Great Paper on HSAemu Full system simulator built form PQUEMU to do Full System Emulation of HSA from our Academic Member Yeh-Ching Chung of National Tsing Hua University
PEARC17: Interactive Code Adaptation Tool for Modernizing Applications for In...Ritu Arora
Often, HPC software outlives the HPC systems for which they are initially developed. The innovations in the HPC platforms’ hardware and parallel programming standards drive the modernization of HPC applications so that they continue being performant. While such code modernization efforts may not be very challenging for HPC experts and well-funded research groups, many domain-experts may find it challenging to adapt their applications for latest HPC platforms due to lack of expertise, time, and funds. The challenges of such domain-experts can be mitigated by providing them high-level tools for code modernization and migration.
Exploiting Linux Control Groups for Effective Run-time Resource ManagementPatrick Bellasi
Emerging multi/many-core architectures, targeting both High Performance Computing (HPC) and mobile devices,
increase the interest for self-adaptive systems, where both applications and computational resources could smoothly adapt
to the changing of the working conditions. In these scenarios, an efficient Run-Time Resource Manager (RTRM) framework
can provide a valuable support to identify the optimal trade-off between the Quality-of-Service (QoS) requirements of the
applications and the time varying resources availability.
This presentation introduces a new approach to the development of a system-wide RTRM featuring: a) a hierarchical and distributed control, b) the exploitation of design-time information, c) a rich multi-objective optimization strategy and d) a portable and modular design based on a set of tunable policies. The framework is already available as an Open Source project, targeting a NUMA architecture and a new generation multi/many-core research platform. First tests show benefits for the execution of parallel applications, the scalability of the proposed multi-objective resources partitioning strategy, and the sustainability of the overheads introduced by the framework.
Measuring the time spent on small individual fractions of program code is a common technique for analysing performance behavior and detecting performance bottlenecks. The benefits of the approach include a detailed individual attribution of performance and understandable feedback loops when experimenting with different code versions. There are however severe pitfalls when following this approach that can lead to vastly misleading results. Modern dynamic compilers use complex optimisation techniques that take a large part of the program into account. There can be therefore unexpected side-effects when combining different code snippets or even when running a presumably unrelated part of the code. This talk will present performance paradoxes with examples from the domain of dynamic compilation of Java programs. Furthermore, it will discuss an alternative approach to modelling code performance characteristics that takes the challenges of complex optimising compilers into account.
Graal and Truffle: Modularity and Separation of Concerns as Cornerstones for ...Thomas Wuerthinger
Multi-language runtimes providing simultaneously high performance for several programming languages still remain an illusion. Industrial-strength managed language runtimes are built with a focus on one language (e.g., Java or C#). Other languages may compile to the bytecode formats of those managed language runtimes. However, the performance characteristics of the bytecode generation approach are often lagging behind compared to language runtimes specialized for a specific language. The performance of JavaScript is for example still orders of magnitude better on specialized runtimes (e.g., V8 or SpiderMonkey).
We present a solution to this problem by providing guest languages with a new way of interfacing with the host runtime. The semantics of the guest language is communicated to the host runtime not via generating bytecodes, but via an interpreter written in the host language. This gives guest languages a simple way to express the semantics of their operations including language-specific mechanisms for collecting profiling feedback. The efficient machine code is derived from the interpreter via automatic partial evaluation. The main components reused from the underlying runtime are the compiler and the garbage collector. They are both agnostic to the executed guest languages.
The host compiler derives the optimized machine code for hot parts of the guest language application via partial evaluation of the guest language interpreter. The interpreter definition can guide the host compiler to generate deoptimization points, i.e., exits from the compiled code. This allows guest language operations to use speculations: An operation could for example speculate that the type of an incoming parameter is constant. Furthermore, the guest language interpreter can use global assumptions about the system state that are registered with the compiled code. Finally, part of the interpreter's code can be excluded from the partial evaluation and remain shared across the system. This is useful for avoiding code explosion and appropriate for infrequently executed paths of an operation. These basic mechanisms are provided by the underlying language-agnostic host runtime and allow separation of concerns between guest and host runtime.
We implemented Truffle, the guest language runtime framework, on top of the Graal compiler and the HotSpot virtual machine. So far, there are prototypes for C, J, Python, JavaScript, R, Ruby, and Smalltalk running on top of the Truffle framework. The prototypes are still incomplete with respect to language semantics. However, most of them can run non-trivial benchmarks to demonstrate the core promise of the Truffle system: Multiple languages within one runtime system at competitive performance.
Updates on the current status of Graal VM, a platform dedicated to run multiple programming languages at excellent performance. Experimental binaries are available from http://www.oracle.com/technetwork/oracle-labs/program-languages/overview/index.html.
Apache Spark has rocked the big data landscape, becoming the largest open source big data community with over 750 contributors from more than 200 organizations. Spark's core tenants of speed, ease of use, and its unified programming model fit neatly with the high performance, scalable, and manageable characteristics of modern Java runtimes. In this talk Tim Ellison, a JVM developer at IBM, shows some of the unique Java 8 capabilities in the JIT compiler, fast networking, serialization techniques, and GPU off-loading that deliver the ultimate big data platform for solving business problems. Tim will demonstrate how solutions, previously infeasible with regular Java programming, become possible with this high performance Spark core runtime, enabling you to solve problems smarter and faster.
Graal is a dynamic meta-circular research compiler for Java that is designed for extensibility and modularity. One of its main distinguishing elements is the handling of optimistic assumptions obtained via profiling feedback and the representation of deoptimization guards in the compiled code. Truffle is a self-optimizing runtime system on top of Graal that uses partial evaluation to derive compiled code from interpreters. Truffle is suitable for creating high-performance implementations for dynamic languages with only moderate effort. The presentation includes a description of the Truffle multi-language API and performance comparisons within the industry of current prototype Truffle language implementations (JavaScript, Ruby, and R). Both Graal and Truffle are open source and form themselves research platforms in the area of virtual machine and programming language implementation (http://openjdk.java.net/projects/graal/).
This covers details of the processes of compilation. A lot of extra teaching support is required with these.
Originally written for AQA A level Computing (UK exam).
Measuring the time spent on small individual fractions of program code is a common technique for analysing performance behavior and detecting performance bottlenecks. The benefits of the approach include a detailed individual attribution of performance and understandable feedback loops when experimenting with different code versions. There are however severe pitfalls when following this approach that can lead to vastly misleading results. Modern dynamic compilers use complex optimisation techniques that take a large part of the program into account. There can be therefore unexpected side-effects when combining different code snippets or even when running a presumably unrelated part of the code. This talk will present performance paradoxes with examples from the domain of dynamic compilation of Java programs. Furthermore, it will discuss an alternative approach to modelling code performance characteristics that takes the challenges of complex optimising compilers into account.
Graal and Truffle: Modularity and Separation of Concerns as Cornerstones for ...Thomas Wuerthinger
Multi-language runtimes providing simultaneously high performance for several programming languages still remain an illusion. Industrial-strength managed language runtimes are built with a focus on one language (e.g., Java or C#). Other languages may compile to the bytecode formats of those managed language runtimes. However, the performance characteristics of the bytecode generation approach are often lagging behind compared to language runtimes specialized for a specific language. The performance of JavaScript is for example still orders of magnitude better on specialized runtimes (e.g., V8 or SpiderMonkey).
We present a solution to this problem by providing guest languages with a new way of interfacing with the host runtime. The semantics of the guest language is communicated to the host runtime not via generating bytecodes, but via an interpreter written in the host language. This gives guest languages a simple way to express the semantics of their operations including language-specific mechanisms for collecting profiling feedback. The efficient machine code is derived from the interpreter via automatic partial evaluation. The main components reused from the underlying runtime are the compiler and the garbage collector. They are both agnostic to the executed guest languages.
The host compiler derives the optimized machine code for hot parts of the guest language application via partial evaluation of the guest language interpreter. The interpreter definition can guide the host compiler to generate deoptimization points, i.e., exits from the compiled code. This allows guest language operations to use speculations: An operation could for example speculate that the type of an incoming parameter is constant. Furthermore, the guest language interpreter can use global assumptions about the system state that are registered with the compiled code. Finally, part of the interpreter's code can be excluded from the partial evaluation and remain shared across the system. This is useful for avoiding code explosion and appropriate for infrequently executed paths of an operation. These basic mechanisms are provided by the underlying language-agnostic host runtime and allow separation of concerns between guest and host runtime.
We implemented Truffle, the guest language runtime framework, on top of the Graal compiler and the HotSpot virtual machine. So far, there are prototypes for C, J, Python, JavaScript, R, Ruby, and Smalltalk running on top of the Truffle framework. The prototypes are still incomplete with respect to language semantics. However, most of them can run non-trivial benchmarks to demonstrate the core promise of the Truffle system: Multiple languages within one runtime system at competitive performance.
Updates on the current status of Graal VM, a platform dedicated to run multiple programming languages at excellent performance. Experimental binaries are available from http://www.oracle.com/technetwork/oracle-labs/program-languages/overview/index.html.
Apache Spark has rocked the big data landscape, becoming the largest open source big data community with over 750 contributors from more than 200 organizations. Spark's core tenants of speed, ease of use, and its unified programming model fit neatly with the high performance, scalable, and manageable characteristics of modern Java runtimes. In this talk Tim Ellison, a JVM developer at IBM, shows some of the unique Java 8 capabilities in the JIT compiler, fast networking, serialization techniques, and GPU off-loading that deliver the ultimate big data platform for solving business problems. Tim will demonstrate how solutions, previously infeasible with regular Java programming, become possible with this high performance Spark core runtime, enabling you to solve problems smarter and faster.
Graal is a dynamic meta-circular research compiler for Java that is designed for extensibility and modularity. One of its main distinguishing elements is the handling of optimistic assumptions obtained via profiling feedback and the representation of deoptimization guards in the compiled code. Truffle is a self-optimizing runtime system on top of Graal that uses partial evaluation to derive compiled code from interpreters. Truffle is suitable for creating high-performance implementations for dynamic languages with only moderate effort. The presentation includes a description of the Truffle multi-language API and performance comparisons within the industry of current prototype Truffle language implementations (JavaScript, Ruby, and R). Both Graal and Truffle are open source and form themselves research platforms in the area of virtual machine and programming language implementation (http://openjdk.java.net/projects/graal/).
This covers details of the processes of compilation. A lot of extra teaching support is required with these.
Originally written for AQA A level Computing (UK exam).
Statistikaamet tutvustas täna, 10. oktoobril leibkonna eelarve uuringu 2015. aasta tulemusi. Statistikaameti analüütik Tiiu-Liisa Rummo andis ülevaate leibkondade sundkulutustest (toidu, mittealkohoolsete jookide ja eluaseme kulud).
Statistikaameti juhtivstatistik-metoodik Karl Viilmann rääkis leibkonna eelarve uuringu tulemuste põhjal leibkonna teistest suurematest kulugruppidest, sh vaba aja, turismi, tervishoiu ja hariduse kulutustest.
Statistikaamet tutvustas 14.11.2016 pressikonverentsil, kuidas toimus registripõhise rahva ja eluruumide loenduse prooviloendus ning milline on selle tulemuste põhjal eluruumide andmete olemasolu ja kvaliteet registrites.
Statistikaameti peadirektori asetäitja Tuulikki Sillajõe rääkis Eesti Raamatupidajate Kogu juubelikonverentsil (25.11.2016) raamatupidamisest, statistika ja unistuste aruandlusest.
Learn more about the tremendous value Open Data Plane brings to NFV
Bob Monkman, Networking Segment Marketing Manager, ARM
Bill Fischofer, Senior Software Engineer, Linaro Networking Group
Moderator:
Brandon Lewis, OpenSystems Media
OpenPOWER Acceleration of HPCC SystemsHPCC Systems
JT Kellington, IBM and Allan Cantle, Nallatech present at the 2015 HPCC Systems Engineering Summit Community Day about porting HPCC Systems to the POWER8-based ppc64el architecture.
"OpenHPC is a collaborative, community effort that initiated from a desire to aggregate a number of common ingredients required to deploy and manage High Performance Computing (HPC) Linux clusters including provisioning tools, resource management, I/O clients, development tools, and a variety of scientific libraries. Packages provided by OpenHPC have been pre-built with HPC integration in mind with a goal to provide re-usable building blocks for the HPC community. Over time, the community also plans to identify and develop abstraction interfaces between key components to further enhance modularity and interchangeability. The community includes representation from a variety of sources including software vendors, equipment manufacturers, research institutions, supercomputing sites, and others."
Watch the video: http://wp.me/p3RLHQ-gKz
Learn more: http://openhpc.community/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Hopsworks at Google AI Huddle, SunnyvaleJim Dowling
Hopsworks is a platform for designing and operating End to End Machine Learning using PySpark and TensorFlow/PyTorch. Early access is now available on GCP. Hopsworks includes the industry's first Feature Store. Hopsworks is open-source.
Big Data Streams Architectures. Why? What? How?Anton Nazaruk
With a current zoo of technologies and different ways of their interaction it's a big challenge to architect a system (or adopt existed one) that will conform to low-latency BigData analysis requirements. Apache Kafka and Kappa Architecture in particular take more and more attention over classic Hadoop-centric technologies stack. New Consumer API put significant boost in this direction. Microservices-based streaming processing and new Kafka Streams tend to be a synergy in BigData world.
Building and deploying LLM applications with Apache AirflowKaxil Naik
Behind the growing interest in Generate AI and LLM-based enterprise applications lies an expanded set of requirements for data integrations and ML orchestration. Enterprises want to use proprietary data to power LLM-based applications that create new business value, but they face challenges in moving beyond experimentation. The pipelines that power these models need to run reliably at scale, bringing together data from many sources and reacting continuously to changing conditions.
This talk focuses on the design patterns for using Apache Airflow to support LLM applications created using private enterprise data. We’ll go through a real-world example of what this looks like, as well as a proposal to improve Airflow and to add additional Airflow Providers to make it easier to interact with LLMs such as the ones from OpenAI (such as GPT4) and the ones on HuggingFace, while working with both structured and unstructured data.
In short, this shows how these Airflow patterns enable reliable, traceable, and scalable LLM applications within the enterprise.
https://airflowsummit.org/sessions/2023/keynote-llm/
Accelerate Big Data Processing with High-Performance Computing TechnologiesIntel® Software
Learn about opportunities and challenges for accelerating big data middleware on modern high-performance computing (HPC) clusters by exploiting HPC technologies.
Hail hydrate! from stream to lake using open sourceTimothy Spann
(VIRTUAL) Hail Hydrate! From Stream to Lake Using Open Source - Timothy J Spann, StreamNative
https://osselc21.sched.com/event/lAPi?iframe=no
A cloud data lake that is empty is not useful to anyone. How can you quickly, scalably and reliably fill your cloud data lake with diverse sources of data you already have and new ones you never imagined you needed. Utilizing open source tools from Apache, the FLiP stack enables any data engineer, programmer or analyst to build reusable modules with low or no code. FLiP utilizes Apache NiFi, Apache Pulsar, Apache Flink and MiNiFi agents to load CDC, Logs, REST, XML, Images, PDFs, Documents, Text, semistructured data, unstructured data, structured data and a hundred data sources you could never dream of streaming before. I will teach you how to fish in the deep end of the lake and return a data engineering hero. Let's hope everyone is ready to go from 0 to Petabyte hero.
https://osselc21.sched.com/event/lAPi/virtual-hail-hydrate-from-stream-to-lake-using-open-source-timothy-j-spann-streamnative
OS for AI: Elastic Microservices & the Next Gen of MLNordic APIs
AI has been a hot topic lately, with advances being made constantly in what is possible, there has not been as much discussion of the infrastructure and scaling challenges that come with it. How do you support dozens of different languages and frameworks, and make them interoperate invisibly? How do you scale to run abstract code from thousands of different developers, simultaneously and elastically, while maintaining less than 15ms of overhead?
At Algorithmia, we’ve built, deployed, and scaled thousands of algorithms and machine learning models, using every kind of framework (from scikit-learn to tensorflow). We’ve seen many of the challenges faced in this area, and in this talk I’ll share some insights into the problems you’re likely to face, and how to approach solving them.
In brief, we’ll examine the need for, and implementations of, a complete “Operating System for AI” – a common interface for different algorithms to be used and combined, and a general architecture for serverless machine learning which is discoverable, versioned, scalable and sharable.
11. ClearSpeed profiler for heterogeneous and multi-processor systems Advance™ Accelerator Board CSX 600 Pipeline CSX 600 Pipeline Host CPU(s) Host CPU(s) Host CPU(s) Advance™ Accelerator Board Host Cores(s) CSX Pipeline HOST/BOARD INTERACTION View host/board interactions. Provides performance information for data transfer operations. Trace cluster node/board interaction. See overlap of host compute and board compute. CSX PIPELINE View detailed instruction issue information. Visualize overlap of executing instructions. Optimize code at the instruction level. View instruction level performance bottlenecks. Get accurate instruction timing. CSX SYSTEM View system level trace. Visually inspect the overlap of compute and I/O. Visualize cache utilization. View branch trace of code executing. Find and analyse performance bottlenecks. Get accurate event timing ClearSpeed Accelerated System CSX Pipeline HOST CODE PROFILING Visually inspect host code executing. Supports multiple threads and processes. Time specific code sections. See overlap of host threads executing. Platform and processor agnostic trace collection. PCIe
So with the scene set for our presentation I’m going to talk a bit about the current state of the art in programming heterogeneous systems (with a summary of what will be used at SARA), as well as taking a look at what the development flow for a heterogeneous system really looks like.
So with the scene set for our presentation I’m going to talk a bit about the current state of the art in programming heterogeneous systems (with a summary of what will be used at SARA), as well as taking a look at what the development flow for a heterogeneous system really looks like.
At SARA the system is based on ClearSpeed Technology hardware and has the full range of development tools and libraries available
The level of support offered by the ClearSpeed SDK for debugging and especially profiling is still well ahead of the best of the rest (for the moment). Host profiling API, allows you to instrument even non-CS specific code and have it displayed in the profiler.
So let’s take a look at what makes heterogeneous systems interesting to the user and also some of the issues involved in programming them.
If it’s single use it’s much easier to justify the investment in time and money to get the benefits of acceleration If it’s multi-use then the cost benefit analysis is more complicated, but can still be swayed by an obvious imbalance in resource consumption. Are the codes yours, open source or closed source ISV applications? If you have source level access do you have the development expertise and resources?
So let’s put closed source applications to one side for a moment. If you have answered yes to “Do you have source access?” and “Do you have the development capabilities?” them, today you will have to decide on one of a number of proprietary development environments.
I include OpenCL here because of it’s similarity to existing languages and it’s imminent availability.
As with MKL, ACML etc IHVs will usually (but not always) get the best out of their hardware. The Library approach is by far and away easiest for the user because it carries with it the potential to provide acceleration for ISV applications, but there are a number of caveats, such as the requirement for the apps to use standard libraries (such as BLAS, LAPACK, FFTW etc) and dynamic linking (many do not because it reduces the support burden). ClearSpeed has long provided a selection of L3 BLAS support and drop in replacements for many of the most popular LAPACK routines. As you will see, the applicability and effectiveness of this approach is limited by the amount of data that gets moved around vs the compute required (in the case of DGEMM that’s n^3 compute to n^2 data)
Ok so we’ve established that proprietary solutions are not ideal for a number of reasons, but even then they have stimulated the interest of the research community and for some cases they still do provide compelling financial advantages to the user. Why do I say ‘inevitably’, well because the pull from both the developers and customers is there. Developers want to innovate, but not all are willing to be locked into single vendor deals for obvious reasons. OpenCL has gained enviable support in a very short period of time and Petapath are members of the Khronos Group and are actively participating on the OpenCL working group.
So what, for those of you who are not familiar with it, is OpenCL? It addresses a wide range of systems in a familiar way. Very similar to the existing language and library support from a number IHVs.
A very interesting point to note here is that OpenCL can also target multi-core systems. It does this via supporting the SIMD extensions to current x86 cores and exposing this parallelism to the developer in a single open API. It doesn’t provide anything that OpenMP doesn’t apart from a single API and programming interface, but this is the huge benefit for developers.
Note that there can be multiple OpenCL compute devices in a single system. Initially this is likely to be the host multi-core backend and a single vendor’s accelerator but the potential is there for supporting multiple accelerators and incrementally accelerating your systems.
So this all sounds great, but when will I be able to use OpenCL. And it’s a 1.0 spec shouldn’t I watch to see what happens for a little bit?
Note that I said earlier that there could be multiple OpenCL supported devices in a system. Well interoperability between different vendor’s implementations will be the key to this.
So having mapped out what people use today, and what standards we may have in the near future what does development on a heterogeneous system look like today?
Well if you’re here then there is probably a financial or scientific imperative to make the application run faster. HVs also provide optimised (BLAS, LAPACK, etc.) so use where you can Many compilers enable support for SSE2+ and auto-parallelisation Does it run fast enough yet? (Where can I go next if it doesn’t?)
The list of general and vendor specific tips is too long to go into here.
Wake up Tim I’m expecting a heckle here!
So having mapped out what people use today, and what standards we may have in the near future what does development on a heterogeneous system look like today?
So will all vendors hardware behave the same? How will the performance vary on different platforms?
(clearspeed gdb support and has done for about four years)
(clearspeed gdb support and has done for about four years)
There are many tools that developers rely on for host development and I think that means there will be space for a thriving ecosystem of third party tools for OpenCL