Posters for ‘LONGLEGS’, starring @MaikaMonroe and Nicolas Cage. In theaters 
on July 12.

Melissa Barrera and Jenna Ortega left the Scream franchise before Scream 7, and fans wonder what will happen to Sam and Tara Carpenter.

How Can Scream 7's Plot Address Melissa Barrera And Jenna Ortega's Exits?

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After being fired from the &#x27;Scream&#x27; franchise, Melissa Barrera reclaims her scream queen status with &#x27;Abigail,&#x27; the latest campy, gory romp from Radio Silence Productions.

Melissa Barrera talks 'Abigail,' being fired from 'Scream' - Los Angeles Times

Scream (2022) and Scream VI star Melissa Barrera, who was fired from Scream 7, would be interested in joining Scary Movie 6

Former Scream star Melissa Barrera wants to be in Scary Movie 6

Matt Bettinelli-Olpin and Tyler Gillett explain why they chose to cast [SPOILER] as the Dracula-esque crimelord Kristof Lazar in the bloodsoaked flick

Scream directors open up on that unexpected cameo in new vampire horror movie Abigail | GamesRadar+

Horror movies are seeing a major uptick in quanitity, and Universal’s Abigail is the latest in this trend with a vampiric ballerina. Directed by Radio Silence (Matt Bettinelli-Olpin and Tyler Gillett), the film sees the directors re-team with Melissa Barrera, who starred in their two Scream ...

‘Abigail’ Star Melissa Barrera On Reuniting With Her ‘Scream’ And ‘Scream VI’ Directors For Ballerina Vampire Film

SQream

sqream

HBO’s comedy series Silicon Valley received critical acclaim and a near-perfect score on Rotten Tomatoes. Like most great comedies, the show’s success is likely thanks to its ability to offer a satirical take on true-to-life scenarios on some of the more outlandish elements of the Bay Area tech scene between 2014-2019. In this case, the founder of Pied Piper and his team are on a mission to bring his novel compression technology out into the world where it can scale to the masses. Back when the series was in production, this compression technology wasn’t much more than a clever storytelling device that served to show what a niche this scrappy startup had found with its groundbreaking solution. Yet if we fast forward to 2024, with the boom in GenAI technology and unprecedented demand for data, there’s never been more urgent need for a solution akin to that put forward by the creators of Silicon Valley. However, it seems like one company has succeeded in creating a surprisingly similar compression model that looks to break through the huge data volumes at play today. To understand Silicon Valley, watch Silicon Valley In the series, Pied Piper is a compression startup that provides instant access to your data. And it’s far from the only example of life imitating art. Indeed, the concept of a data compression tool isn’t the only element of Pied Piper that crossed over into reality. In fact for the research of the show professors were consulted, with the Weissman Score being a new compression metric developed for the series itself. However, this didn’t stop real-world researchers from actually using it. Meanwhile, Vinith Misra, a researcher at IBM’s Watson Group consulted for the show, went so far as to write a technical paper that offered a fictional improvement to the compression algorithm. However, while the compression algorithm put forward for the show was fiction, a powerful new breed of GPUs and advanced processing chips mean that next-level data processing capabilities are now within the realms of possibility. Bringing Pied Piper’s dream into reality While a few companies are getting closer to earning the title of the ‘real Pied Piper’ for data compression, one New York startup is garnering plenty of attention of late for its strides forward in this specialism. SQream Blue, the data lakehouse solution from the data acceleration platform, recently outperformed similar solutions on the market by a country mile. When it comes to speed and cost, SQream Blue and its patented technology compression solution performed 3 times faster and at half the cost of Databricks during the comparison element of the test. To put this into closer detail, SQream Blue’s total runtime was 2462.6 seconds, with the total cost for processing the data end-to-end being $26.94. Databricks’ total runtime was 8332.4 seconds, at a cost of $76.94, indicating a significant cost-performance advantage by SQream Blue for big data analysis. To get these results, SQream ran the benchmark on Amazon Web Services (AWS) with a scale factor of 30,000, which creates a dataset of around 30 TB, to test the relative capabilities of SQream at scale. All generated data was stored as Apache Parquet files on Amazon Simple Storage Service (Amazon  S3), and the queries were processed without pre-loading into a database. According to the announcement, the SQream Blue’s unparalleled speed was equivalent to reading every cataloged book in the US Library of Congress in under an hour – and then buying them all for less than $25, giving a close reminder of Pied Piper’s standout results on the Weissman test on the infamous Silicon Valley episode at TechCrunch Disrupt. In 2024, these kinds of gains can’t come quickly enough. Tackling our data addiction Gavin Belson, billionaire senior executive and main antagonist on Silicon Valley, delivered a telling rant on this subject, “Data creation is exploding. With all the selfies and useless files people refuse to delete on the cloud, 92 percent of the world’s data was created in the last two years alone. At the current rate, the world’s data storage capacity will be overtaken by next spring. It will be nothing short of a catastrophe.” “Data shortages, data rationing, data black markets. Someone’s compression will save the world from data-geddon, and it sure as hell better be Nucleus and not goddamn Pied Piper!” But this isn’t just about selfies and content. Public clouds now host more than half of all enterprise workloads and some businesses spend upwards of $12 million annually on that space. This represents a huge amount of business-critical data that is at the point of being unsustainable to manage using current methods. It’s for this reason that the real world does need a Pied Piper. HBO’s comedy series Silicon Valley received critical acclaim and a near-perfect score on Rotten Tomatoes. Like most great comedies, the show’s success is likely thanks to its ability to offer a satirical take on true-to-life scenarios on some of the more outlandish elements of the Bay Area tech scene between 2014-2019. In this case, the founder of Pied Piper and his team are on a mission to bring his novel compression technology out into the world where it can scale to the masses. Pied Piper Back when the series was in production, this compression technology wasn’t much more than a clever storytelling device that served to show what a niche this scrappy startup had found with its groundbreaking solution. Yet if we fast forward to 2024, with the boom in GenAI technology and unprecedented demand for data, there’s never been more urgent need for a solution akin to that put forward by the creators of Silicon Valley. However, it seems like one company has succeeded in creating a surprisingly similar compression model that looks to break through the huge data volumes at play today. To understand Silicon Valley, watch Silicon Valley To understand Silicon Valley, watch Silicon Valley In the series, Pied Piper is a compression startup that provides instant access to your data. And it’s far from the only example of life imitating art. Indeed, the concept of a data compression tool isn’t the only element of Pied Piper that crossed over into reality. In fact for the research of the show professors were consulted, with the Weissman Score being a new compression metric developed for the series itself. However, this didn’t stop real-world researchers from actually using it. real-world researchers Meanwhile, Vinith Misra, a researcher at IBM’s Watson Group consulted for the show, went so far as to write a technical paper that offered a fictional improvement to the compression algorithm. technical paper However, while the compression algorithm put forward for the show was fiction, a powerful new breed of GPUs and advanced processing chips mean that next-level data processing capabilities are now within the realms of possibility. Bringing Pied Piper’s dream into reality Bringing Pied Piper’s dream into reality While a few companies are getting closer to earning the title of the ‘real Pied Piper’ for data compression, one New York startup is garnering plenty of attention of late for its strides forward in this specialism. SQream Blue, the data lakehouse solution from the data acceleration platform, recently outperformed similar solutions on the market by a country mile. When it comes to speed and cost, SQream Blue and its patented technology compression solution performed 3 times faster and at half the cost of Databricks during the comparison element of the test. To put this into closer detail, SQream Blue’s total runtime was 2462.6 seconds, with the total cost for processing the data end-to-end being $26.94. Databricks’ total runtime was 8332.4 seconds, at a cost of $76.94, indicating a significant cost-performance advantage by SQream Blue for big data analysis. To get these results, SQream ran the benchmark on Amazon Web Services (AWS) with a scale factor of 30,000, which creates a dataset of around 30 TB, to test the relative capabilities of SQream at scale. All generated data was stored as Apache Parquet files on Amazon Simple Storage Service (Amazon  S3), and the queries were processed without pre-loading into a database. According to the announcement, the SQream Blue’s unparalleled speed was equivalent to reading every cataloged book in the US Library of Congress in under an hour – and then buying them all for less than $25, giving a close reminder of Pied Piper’s standout results on the Weissman test on the infamous Silicon Valley episode at TechCrunch Disrupt . TechCrunch Disrupt In 2024, these kinds of gains can’t come quickly enough. Tackling our data addiction Tackling our data addiction Gavin Belson, billionaire senior executive and main antagonist on Silicon Valley, delivered a telling rant on this subject, “Data creation is exploding. With all the selfies and useless files people refuse to delete on the cloud, 92 percent of the world’s data was created in the last two years alone. At the current rate, the world’s data storage capacity will be overtaken by next spring. It will be nothing short of a catastrophe.” “Data shortages, data rationing, data black markets. Someone’s compression will save the world from data-geddon, and it sure as hell better be Nucleus and not goddamn Pied Piper!” But this isn’t just about selfies and content. Public clouds now host more than half of all enterprise workloads and some businesses spend upwards of $12 million annually on that space. upwards of $12 million annually This represents a huge amount of business-critical data that is at the point of being unsustainable to manage using current methods. It’s for this reason that the real world does need a Pied Piper.

Silicon Valley’s Pied Piper is Now Real Thanks to New Compression Technology

Silicon Valley’s Pied Piper ukax Jichhax Chiqpach Machaq Tecnología de Compresión ukar yuspajarasawa

Pied Piper от Силиконовата долина вече е истински благодарение на новата технология за компресиране

El flautista de Hamelin de Silicon Valley ahora es una realidad gracias a la nueva tecnología de compresión

Silicon Valley-ko Pied Piper orain benetakoa da Konpresio Teknologia Berriari esker

Silicon Valleyn Pied Piper on nyt todellinen uuden pakkaustekniikan ansiosta

Սիլիկոնային հովտի Pied Piper-ն այժմ իրական է նոր սեղմման տեխնոլոգիայի շնորհիվ

シリコンバレーのパイドパイパーが新しい圧縮技術のおかげで現実に

Silicon Valley’s Pied Piper Bjale ke ya Kgonthe Ka lebaka la Theknolotši ye Mpsha ya Kgateletšo

Pied Piper din Silicon Valley este acum real datorită noii tehnologii de compresie

Silicon Valley's Pied Piper Hadda waa dhab Thanks to Tiknoolajiyada Isku-buufinta Cusub

Пиед Пипер из Силицијумске долине је сада стваран захваљујући новој технологији компресије

சிலிக்கான் வேலியின் பைட் பைபர் இப்போது புதிய சுருக்க தொழில்நுட்பத்திற்கு நன்றி

Computing on the Edge: How GPUs are Shaping the Future

As AI continues to take over our imagination and industries, another trend has been quietly growing behind the scenes. Graphics Processing Units (GPUs) are a type of advanced processing chip that, until recently, was heavily leveraged by the gaming industry and other highly visual use cases, originally designed to accelerate computer image processing. However, the recent boom in generative AI and the reliance on LLMs means that GPUs are now becoming more common here. As leaders, CTOs and data specialists turn to these powerful chips to boost their big data projects, 2024 is the year that GPUs will further fuel the AI revolution. If there is any question about GPU’s new dominance, it can be found in the numbers. The global market for GPUs is already valued at a huge $65.3 billion, projected to increase nearly five-fold by the end of the decade. Meanwhile, Nvidia, a name already synonymous with the AI revolution, has been generating a large part of its recent revenue boom thanks to AI-accelerating chips like the H200 Tensor Core GPU that are particularly popular with CTOs. Yet why are GPUs now the tour-de-force for AI projects? What has fueled the migration away from image processing to AI accelerators? Let’s take a look at three of the major advantages of GPUs for AI projects. Getting skyrocketing costs under control For AI to be successful it must also deliver well against cost-performance metrics. However, many CIOs have found in recent years that simply adding more CPUs to increase the abilities of their big data models has caused expenditure to skyrocket. As a result, AI projects are either being shelved due to out-of-proportion costs vs the business results delivered, or projects are being compromised by reducing the number of queries or scope of the analysis. These approaches will only undermine the future of AI for companies by failing to deliver the positive business outcomes and operations insights that made leaders take notice of the technology in the first place. The scale of this trend was put into sharp focus by a new 2024 State of Big Data Analytics Report published this week by SQream, a data acceleration specialist startup from New York. Based on a survey with 300 data experts at enterprise-level organizations across the US, the report noted that 71% regularly experience ‘bill-shock’ associated with their data projects. Yet despite the fact that all survey respondents had a budget of over $5BN, 98% still experience AI project failure with poor ‘cost-performance’ as a major contributor to these failures. The report also pointed towards a GPU-driven future for enterprise data architectures, with three in four execs noting that they are looking to GPUs in the future to realize more impact and value from their projects. By using GPUs, CTOs are able to solve the cost-performance challenge that’s rampant across AI initiatives by upping the computing ability of the infrastructure using GPUs. From the time needed to process huge data sets to the number of queries being run, these powerful chips can accelerate the pace of work, overcome logistical hurdles and reach results more quickly resulting in major savings for companies. Creating new compute possibilities The rise of GPUs across the AI industry also means a decline in the reliance of CPUs, the previous industry standard for data processing tasks. CPUs were designed for sequential processing, which means that tasks and queries will be actioned in the exact order they were queued in. This approach is well-equipped to handle small data tasks quickly, meaning they have sufficed for many data projects of the past decade. However, in 2024 AI projects need to handle huge volumes of complex, often unstructured, data sets handling detailed queries in a non-linear fashion. CPUs fall short when it comes to such complex workloads, which is why many experts have turned to GPUs instead. While saving costs to ensure AI projects can be maintained is a major advantage, GPUs are also creating entirely new possibilities thanks to their advanced capabilities. These chips have thousands of cores optimized for parallel processing, which gives them the ability to process vast amounts of data simultaneously. Further, they can scale up to supercomputing levels, and they can draw from a broad and deep software stack. This makes them perfect partners for enterprise projects and collaborative industry partnerships that come with more complex data infrastructure or fueling other use cases like virtual reality suites or digital twins that boost AI projects even further. Shrinking AI’s carbon footprint Finally, the environmental cost of AI is becoming increasingly clear. Although algorithms aren’t an obvious source of carbon emissions in the way that a diesel engine spewing fumes would be, each query comes with an associated carbon cost. For example, training Chat-GPT 3 and its LLM created 500 tons of carbon dioxide. In addition, according to McKinsey more than 90% of S&P 500 companies and 70% of Russell 1000 companies now publish Environmental, Sustainability and Governance (ESG) reports in some form. Organizations that have plowed ahead with AI adoption will need to ensure that its associated carbon cost is declared in these reports. Indeed, in a 2024 report from Deloitte titled The State of Digital Adoption, there is a growing demand for sustainable and resilient tech infrastructure. For this reason, it’s important to note that by incorporating GPUs into their wider tech stack, leaders can reduce their AI carbon footprint immediately. Relying on an outdated tech stack means that companies will inherently be using more power and energy to maintain them. Much like upgrading an old diesel car for a sleek new hybrid is an environmentally friendly switch, the overall efficiency gains that come with changing from CPUs to GPUs equates to lower environmental impact. A future of strategic GPU application The case for adopting GPUs to support AI use cases is growing stronger by the day. However, as their myriad advantages become common knowledge, demand will grow in turn. CIOs need to ensure they move quickly and strategically deploy GPUs, partnering with data experts where needed to leverage other complementary strategies that accelerate the efficiency of their data infrastructure. As AI continues to take over our imagination and industries, another trend has been quietly growing behind the scenes. As AI continues to take over our imagination and industries, another trend has been quietly growing behind the scenes. Graphics Processing Units ( GPUs ) are a type of advanced processing chip that, until recently, was heavily leveraged by the gaming industry and other highly visual use cases, originally designed to accelerate computer image processing. GPUs However, the recent boom in generative AI and the reliance on LLMs means that GPUs are now becoming more common here. As leaders, CTOs and data specialists turn to these powerful chips to boost their big data projects, 2024 is the year that GPUs will further fuel the AI revolution. If there is any question about GPU’s new dominance, it can be found in the numbers. The global market for GPUs is already valued at a huge $65.3 billion , projected to increase nearly five-fold by the end of the decade. $65.3 billion $65.3 billion Meanwhile, Nvidia, a name already synonymous with the AI revolution, has been generating a large part of its recent revenue boom thanks to AI-accelerating chips like the H200 Tensor Core GPU that are particularly popular with CTOs. the H200 Tensor Core GPU the H200 Tensor Core GPU popular popular Yet why are GPUs now the tour-de-force for AI projects? What has fueled the migration away from image processing to AI accelerators? Let’s take a look at three of the major advantages of GPUs for AI projects. Getting skyrocketing costs under control For AI to be successful it must also deliver well against cost-performance metrics. However, many CIOs have found in recent years that simply adding more CPUs to increase the abilities of their big data models has caused expenditure to skyrocket. As a result, AI projects are either being shelved due to out-of-proportion costs vs the business results delivered, or projects are being compromised by reducing the number of queries or scope of the analysis. These approaches will only undermine the future of AI for companies by failing to deliver the positive business outcomes and operations insights that made leaders take notice of the technology in the first place. The scale of this trend was put into sharp focus by a new 2024 State of Big Data Analytics Report published this week by SQream, a data acceleration specialist startup from New York. Based on a survey with 300 data experts at enterprise-level organizations across the US, the report noted that 71% regularly experience ‘bill-shock’ associated with their data projects. new 2024 State of Big Data Analytics Report new 2024 State of Big Data Analytics Report Yet despite the fact that all survey respondents had a budget of over $5BN, 98% still experience AI project failure with poor ‘cost-performance’ as a major contributor to these failures. The report also pointed towards a GPU-driven future for enterprise data architectures, with three in four execs noting that they are looking to GPUs in the future to realize more impact and value from their projects. By using GPUs, CTOs are able to solve the cost-performance challenge that’s rampant across AI initiatives by upping the computing ability of the infrastructure using GPUs. From the time needed to process huge data sets to the number of queries being run, these powerful chips can accelerate the pace of work, overcome logistical hurdles and reach results more quickly resulting in major savings for companies. Creating new compute possibilities The rise of GPUs across the AI industry also means a decline in the reliance of CPUs, the previous industry standard for data processing tasks. CPUs were designed for sequential processing, which means that tasks and queries will be actioned in the exact order they were queued in. This approach is well-equipped to handle small data tasks quickly, meaning they have sufficed for many data projects of the past decade. However, in 2024 AI projects need to handle huge volumes of complex, often unstructured, data sets handling detailed queries in a non-linear fashion. CPUs fall short when it comes to such complex workloads, which is why many experts have turned to GPUs instead. While saving costs to ensure AI projects can be maintained is a major advantage, GPUs are also creating entirely new possibilities thanks to their advanced capabilities. These chips have thousands of cores optimized for parallel processing, which gives them the ability to process vast amounts of data simultaneously. Further, they can scale up to supercomputing levels, and they can draw from a broad and deep software stack. This makes them perfect partners for enterprise projects and collaborative industry partnerships that come with more complex data infrastructure or fueling other use cases like virtual reality suites or digital twins that boost AI projects even further. Shrinking AI’s carbon footprint Finally, the environmental cost of AI is becoming increasingly clear. Although algorithms aren’t an obvious source of carbon emissions in the way that a diesel engine spewing fumes would be, each query comes with an associated carbon cost. For example, training Chat-GPT 3 and its LLM created 500 tons of carbon dioxide. 500 tons 500 tons In addition, according to McKinsey more than 90% of S&P 500 companies and 70% of Russell 1000 companies now publish Environmental, Sustainability and Governance (ESG) reports in some form. Organizations that have plowed ahead with AI adoption will need to ensure that its associated carbon cost is declared in these reports. McKinsey McKinsey Indeed, in a 2024 report from Deloitte titled The State of Digital Adoption , there is a growing demand for sustainable and resilient tech infrastructure. For this reason, it’s important to note that by incorporating GPUs into their wider tech stack, leaders can reduce their AI carbon footprint immediately. The State of Digital Adoption The State of Digital Adoption Relying on an outdated tech stack means that companies will inherently be using more power and energy to maintain them. Much like upgrading an old diesel car for a sleek new hybrid is an environmentally friendly switch, the overall efficiency gains that come with changing from CPUs to GPUs equates to lower environmental impact. A future of strategic GPU application The case for adopting GPUs to support AI use cases is growing stronger by the day. However, as their myriad advantages become common knowledge, demand will grow in turn. CIOs need to ensure they move quickly and strategically deploy GPUs, partnering with data experts where needed to leverage other complementary strategies that accelerate the efficiency of their data infrastructure.

Why GPUs Offer a Major Edge for AI Adopters in 2024

As the resident scientist (fun fact, my Bachelor’s is in Chemistry) in my workplace, I was recently asked to write something for Women's Science Day. As I reflected, I realized how big of a piece science still plays in my life, albeit indirectly, and how it reflects in my day-to-day job and my relationship with SQream (my place of employment). Science isn’t just about calculations or mixing substances in a lab, it’s not just developing a drug or discovering a new type of fish that no one has ever seen before, science is a way of thinking. It is the desire to analyze and explore the world around us, each in its own way. It’s the ability to look at our core assumptions every day and to stop for a moment to examine them, where they come from, where we learned them and how they improve us, or more critically, harm us. One of these assumptions, by the way, is the assumption that girls aren’t as good at STEM or suitable for that world as boys. Thinking further on this,  I found another way this line of thought connects to feminism, which is very close to my heart. What are my core assumptions that have evolved due to decades and centuries in which it was illegal for women to engage in science? Obviously, they will think they can't, we didn't let them do it so there's not a vast enough history and well-known names in the field. Everyone knows about Einstein or Newton, we all know that the test of whether a computer is human-like is called the Turing Test (after Alan Turing who is considered the father of modern computers), but how many of us know who or are? Hedy Lamarr Ada Lovelace Our entire lives today are based on computing and programming and broadcasting far and wide, and they were pioneers in their fields. in the case of Lamarr, while also being a successful actress. Scientific spaces belong to us just as they belong to everyone else, regardless of gender. This can also be seen in the world of high-tech, we see fewer women in key positions because we have our assumption that they are less suitable, although there are dozens of studies in the field that show the integration of women into teams and management raises productivity. There's also another reason why we think women are less suited to an intense life of management or science. The frequent expectation is that women would give up their career aspirations away in favor of raising children. To this day there is not necessarily such an expectation from fathers. The thought of one day raising a family is connected to one of my biggest anxieties: Climate anxiety. My scientific side really likes to delve into the subject, read the UN reports in the field, be interested in who and what is causing the very harsh changes that await us not so far down the line. I can't see myself condemning a new person to a world that's going to fall apart, and my only way to prevent that is to spread awareness of research on the subject and actively promote alternatives. Recycling another bottle doesn't matter when there are dozens of big companies that produce a lot more plastic than I do. Working for a company that promotes an alternative causing less of an energy overload might do something. The desire to do what I can to leave behind the world better and more livable than the one I was given is one of the reasons I work where I work.  Even if I no longer think it will be by developing an important cure or chemical substance like I once thought, here I have the place to minimize the damage a little bit. We all live in this world, with all its consequences and assumptions and expectations, and we have nowhere else to go. We don't have enough time to let another false premise stop us from doing what we can to leave behind a better world, both for ourselves and for what comes after us, no matter how much we were not given this opportunity in the past. So, happy Women's Science Day to us all, may it be a day of the revolution, a day of light and hope for a better future, a day when we will not let anything stop us from moving forward.

Yes, Women Also Belong in STEM

Hardware acceleration for data processing has a long history. It’s not new. When I was a child, my dad outfitted our 12 MHz 286 system with the optional 80287 Floating Point Unit (FPU) coprocessor, which ran at a whopping 4.7MHz. The thing was a beast. It did wonders for Lotus 1–2–3. Not long after, this specific FPU was integrated into the CPU, reducing the need for the external coprocessor for math operations. However, coprocessor actually stuck around for longer. You know that GPU you’re using to read this article? DSPs in your mobile phone? Sound card? They’re all coprocessors, and there’s a reason they’re still around. Your CPU can’t do everything No, really. It can’t. The actual silicon for the Intel x86 (the heir to the 8086 and my beloved 80286) architecture is already crazy complex. Adding additional operations to the x86 architecture has diminishing returns, according to Rick Merrit of the EETimes. to the point where it may not be worth it In fact, GlobalFoundries, one of the largest silicon chip manufacturers has announced it’s . It’s just not worth it anymore. abandoning plans for 7nm chips Intel Haswell-EP Xeon E5–2699 V3 Die on wccftech Why is that a problem? Well, each processor core in the x86 architecture does so many different things, that it has actually made programmers complacent. Don’t worry about writing bad software — the processor will make sure it runs fast! Of course, Intel already knew this back in the early 2000s. They tried to kill off the x86 architecture with Itanium, a processor series designed for 64-bit computing. However, AMD had a different plan, and released 64-bit capable x86 processors, obstructing Intel’s plans. And that’s how we ended up with this mess. Going multicore The only way to get more oomph out of CPUs was to scale them out. That’s why today’s CPUs are almost always multi-core. They have anywhere from 2 to 28 cores for higher end server chips like the Intel Xeon series. The clever pipelining and vectorization, caching, and some other complex systems found in the x86 architecture allow for pretty good use of the miniature electronics. Some tasks can run in groups, and the processor may even correctly predict what the next actions are, before they happen. However, as I previously mentioned — these processors are already crazy complex. Other architectures do exist. ARM, which you can find in a huge variety of devices — from battery chargers to your phone, and even your car — has a reduced instruction set, also known as RISC. IBM has similarly gone the RISC route. RISC processors have a much simpler core, which can’t do as much as more elaborate CPU cores. Software has to be specifically written to be multi-core to benefit from these kinds of processors. For now, x86 has a stay of execution. It could be another 10, 15 years, but x86 will be going away. If you’re interested in finding out more about what Intel was doing to enable parallel processing, read “The Story of ISPC”, about the Intel SPMD Program Compiler: https://pharr.org/matt/blog/2018/04/18/ispc-origins.html Here come the GPUs and GPGPUs Graphical Processing Units (GPUs) are also not really new. The term has , but tended to focus strictly on graphics. NVIDIA’s first card marketed as a “GPU” was the GeForce 256, from 2009. However, General Purpose GPUs ( s) actually started appearing around 2007, when NVIDIA and ATI (now AMD) started equipping their 3D graphics cards with more and more capabilities, like Unified Pixel Shaders. These capabilities could be repurposed to perform operations like matrix multiplications, Fast Fourier Transform, wavelet transform, and more. been in use since 1986 at least GPGPU NVIDIA Tesla C870 with 128 CUDA cores ( ) NVIDIA Accessing these features meant that new programming constructs had to be invented. So, in 2008, Apple started developing OpenCL. By late 2009 it was already adopted by AMD, IBM, Qualcom, Intel, and even NVIDIA. AMD decided to support OpenCL extensively, instead of their “Close to Metal” framework. AMD’s Radeon Pro V340, with 512GB/s of memory bandwidth. Source: AMD While NVIDIA supported and still supports OpenCL, they didn’t abandon their own framework, known as CUDA. Today, CUDA is the de-facto standard for high-speed, high-throughput GPU computing. It’s essentially a software layer that gives direct access to the GPU’s virtual instruction set and parallel computational elements, for the execution of compute kernels. Newer NVIDIA GPUs have up to 5,760 CUDA cores With 256 cores in 2009, and with up to 5,760 cores in a single modern GPU, I think it’s easy to see why GPUs are useful for operations that require a high degree of parallelism. But it’s not just parallelism — GPUs have extremely large memory bandwidth, which makes it suitable for high-throughput applications. Why don’t we just replace CPUs with GPUs? Not all applications are suitable for GPUs. GPUs are mostly useful if: The actions are repetitive The actions are mostly independant (do not rely on eachother) The actions are computationally intensive GPUs have found some interesting uses outside of graphics (and cryptomining). Today, GPUs are found in a variety of data processing scenarios. They can be used as part of a data processing pipeline, for running machine learning models on, as part of a hardware accelerated relational database, or just render a result nicely. Let’s take a look at three of the main areas where GPUs can help in the data processing pipeline: GPUs for Stream Processing New stream processing solutions, like ’s Plasma Engine can take advantage of GPUs for stream processing data coming in and out of databases (GPU or not). This tool can be used to perform analysis and/or transformation of streaming data on the GPU. FASTDATA.io The main competitor to FASTDATA’s engine is , which is available as an open-source add-on from IBM. GPU-enabled Spark GPU Databases for Analytics Practically all GPU databases are ground-up, purpose built for analytics. GPU Databases. Source: The 2018 Big Data Landscape The reason for building a GPU database from scratch seems to be the difficulty in retrofitting an old, row-based database with a very different technologies. There are several players in the GPU database field, each with their own benefits: OmniSci (formerly MapD) — In-memory GPU database for geospatial use-cases SQream DB — GPU data warehouse for larger-than-RAM data-sets BlazingDB — GPU database based on Parquet Kinetica — In-memory GPU database HeteroDB — Postgres plugin, based on UDFs for fast NVMe access Brytlyt — Postgres plugin for in-memory compute Blazegraph — Graph database GPUs for machine learning Machine learning tends to be highly suitable for GPU architecture. It’s not just about the high level of parallelism, and the multitude of matrix operations. A big part of the reason is actually the aforementioned memory bandwidth. NVIDIA Tesla V100 core-arrangement. Source: ServeTheHome CPUs are what we call latency-oriented processors. Latency-oriented means that they favor lower latency of operations with clock cycles in the nanoseconds (~3 billion operations per second on one piece of data). In contrast, GPUs are throughput oriented. They will perform a single operation over lots of data at once, at a lower clock speed (~1 billion operations per second on multiple chunks of data). GPUs sacrifice latency with a slower clock speed, in order to get a higher throughput on every clock cycle. There is no shortage in different GPU-enabled machine learning frameworks: TensorFlow cuBLAS Caffe Theano Torch7 cuDNN MATLAB cxxnet/mxnet Deeplearning4j Keras Mathematica and many more. Not just GPUs — ASICs, FPGAs and other exotic hardware While CPUs and GPUs are general purpose, we have some other alternatives in hardware, but there are trade-offs. We’ve already discussed CPUs and GPUs, so let’s talk about FPGAs and ASICs specifically. FPGAs Field Programmable Gate Arrays (FPGA) are a high-speed, programmable chip. They’re extremely popular for prototyping and specialist devices. They contain an array of programmable logic blocks, and a hierarchy of reconfigurable interconnects. Because FPGAs are programmable, they can be altered after they’ve been manufactured, but not by anyone. They provide good performance, but can cost quite a bit — which is why they remain mostly in low-yield devices. You can find FPGAs in medical equipment, cars, specialist equipment, etc. FPGAs provide higher memory bandwidth, lower power consumption than CPUs (and GPUs), but they may struggle with floating point computations, and are hard to work with. In fact, they’re quite difficult to program for, even though recent tooling like OpenCL for FPGA exists. In data analytics, FPGAs are suited for simple repetitive tasks. They’re found in modern platforms like , the , the , and devices like . One of the most well-known usage of FPGAs in analytics is , although the FPGA aspect of Netezza seems to have been mostly abandoned. Microsoft’s Project Brainwave Swarm64 Database Accelerator for Postgres Xilinx Alveo Data Center Accelerator Ryft IBM’s Netezza Eventually, Netezza ended up as part of and the FPGA components were used primarily for specific operations that would benefit from FPGAs like decompression and transformations. Other operations still used the CPU. IBM PureData Source: IBM Data Retrieval Technologies RDBMS, BLU, IBM Netezza and Hadoop ASICs Custom designed Application-Specific Integrated Circuits (ASIC) are the fastest option for high-speed operations, in the silicon level. Because these chips are highly customized, they can deliver the best performance with minimal overhead. ASICs are commonly found as digital signal processors (DSPs) — in audio processing, video encoding, and networking among others. These applications are primarily in mass-produced devices like cameras, mobile phones, wireless routers, etc. Because of the high cost and time investment in creating custom ASICs, they are also the most costly option. They are actually the cheaper option if you plan on manufacturing millions of them, but at smaller volumes they are hard to justify. For that reason, they’re not commonly found in analytics or data processing proper. Comparing the tradeoffs Here’s a summary of the different processors and their tradeoffs: Each of the processor types has its own tradeoffs The future of hardware accelerated data processing The future is in hardware diversity, but not all of the coprocessors are suitable for every task. : Keep standard, sequential programs and code running The CPU’s job Run high-throughput parallel code The Coprocessor’s job: Increase the throughput of parallel programs CPU + Coprocessor: It’s clear that the x86 CPU architecture will not last forever. It simply is not sustainable for the long-run. It’s not advancing at the same rate that modern data workloads are. Because they’re not bound to the same backwards compatibility, GPUs are advancing much faster. Modern data workloads are varied, varying, growing, and fast-moving. GPU and other hardware accelerated data processing applications are growing in popularity, with some notable successes in the machine learning space and data warehousing space. Tesla V100 with NVLink GPU-to-GPU and GPU-to-CPU (Power9) connections. Source: NVIDIA FPGA and GPU-enabled hardware, like IBM Power9 with NVLINK and OpenCAPI, as well as Azure and AWS GPU and FPGA instances are just a sign of things to come. Conclusion I believe hardware accelerated data processing is still in it’s infancy, but is likely to become more widespread, as the accelerating technologies continue to make strides over today’s CPU architecture. Thanks for reading this to the end! I know it isn’t easy — there’s a lot of information (and acronyms) to digest. This article is the first in a series of hardware accelerated data articles. In my next article, I will deep dive into how different hardware accelerated databases utilize their respective hardware accelerators. If you enjoyed this article, leave a comment below, or share with your friends and colleagues.

A gentle introduction to hardware accelerated data processing

It might seem that retail never changes. At least, that’s how many retailers would like it to be. Instead, retail is constantly shifting — with retailers slow to adopt new technology and strategy. Being slow to adopt new technology but at a certain point, retailers will be left in the dust. isn’t always a bad thing With the rise of online stores and eCommerce, traditional retail stores are forced to adapt to changes and to be more innovative than ever. Online stores present many advantages to their customers: they’re available anywhere and anytime, have “endless aisles” and, for the most part, can predict and analyze customers’ behaviour to offer them products they’re actually interested in. Recently, traditional stores have begun to understand the power of digital technologies and to integrate them directly into their stores. One of the most interesting implementations of new technologies in retail is the use of location analytics to improve customers’ in-store experiences. What is Location Analytics? The most basic definition of location analytics is the ability to gain insight from location data. But that is a bit too simplistic. A more accurate description is as follows: Business data usually contains geographical or location data which mostly goes unused. This data can be as broad as city and country or as specific as GPS location. When this data is placed within the context of big data dashboards and data science models, it allows companies to discover new trends and insights. In retail, location sensing technologies typically require customers to use the retailer’s app and grant it permission to track their location in return for a reward. In this case, the customer’s location is determined through the phone’s GPS or, more accurately, via WiFi or beacons. Examples of Location Analytics in Action Many retailers are already using data analytics to improve the customer experience. One particularly promising opportunity allowed by data analytics is the creation of heat maps. In-store heat maps help retailers have a better idea of customers’ traffic patterns at particular hours of the day or even in real-time. Let’s imagine you’re planning to open a store that sells clothes for teenagers. In order to find out the best location for that specific store, you’ll first want to know which areas teenagers are most likely to shop in. Once you learn which area is best for your target group of consumers, you can and your marketing efforts can be more specific and effective. increase your sales revenue Heat map data can also be used to compare different stores and departments, and can eventually help optimize the retail experience for particular customer segments. This information is useful for making rapid course corrections and for granting customers an improved, more personalized experience. One of the leading providers of Geographic Information Systems (GIS) technologies is . The company’s location-based analytics tools have helped boost their competitive edge, reduce their environmental impact, mitigate their operational risk and lower costs across the board. Esri several retailers , the world’s third largest quick-service hamburger chain, is a great example of a retail company that cleverly implemented Esri’s services for good. Specifically, Wendy’s interest was to to come to a Wendy’s. Location analytics has allowed the staff to easily view sales records and customize demographics on existing restaurants. More than that, Wendy’s was able to predict the value and risks for new and existing restaurant locations. Wendy’s determine how far customers are willing to travel Why Locations Analytics Hasn’t Seen Widespread Adoption Yet Exciting as the possibilities are, retailers have to deal with a crucial obstacle when implementing location analytics in their business strategies, which is getting customers to download and use their apps in the first place. has shown that consumers are willing to share their precious information only to a handful of retail apps. Consumers are, in fact, more likely to download retail apps if they’re compelling enough to use within the store. A study conducted by Harvard Business Review An interesting example of this is Home Depot, which developed an app with store-specific navigation that solved a major customer pain point — difficulty finding items without the assistance of a store employee. Other factors that could lead customers to download a retailer app are special discounts on purchases, convenient payment methods, loyalty programs or even just better assistance over the phone. Why it’s Time to Adopt Location Analytics in Retail Another interference that has limited locations analytics from becoming more widespread is that, until very recently, the computational power needed to analyze location data in real time wasn’t affordable. But today, the GPU SQL databases like , have allowed retailers to process and visualize location data more efficiently. SQream Location analytics provide an immense amount of benefit to all retailers. It can be used to create more accurate shipping estimates, change the products promoted to different areas and more. You could even create heat maps and identify which regions purchase the most expensive products. This can then be used to improve regional performance and in turn overall performance. But why adopt location analytics now rather than five years from now? After all, won’t it only become cheaper and more effective with time? Sure, but you’ll also have dropped behind your competitors by a significant, possibly irrecoverable margin. In retail, it’s important to get ahead of the curve now more than ever. You’re competing against both giants of industry like and smaller, savvy retailers. Both are adopting the latest tech to gain an edge. Amazon If you don’t your orders will slowly dwindle and, if you’re lucky, you’ll be acquired by someone else. Both “too big to fail” brand and smaller niche brands are going out of business left and right. Today’s brick-and-mortar retailers are in a constant race with online competitors. With the results they can gather from location analytics, retailers now focus on innovating and improving their services, while offering their customers an even more personalized experience along their journey. The technology to do it is already here, and retailers who win will be the ones that have built their analytics structure ahead of time.

Location Analytics and Retail — Friends At Last

GPU databases are the hottest new thing in the database world, and it’s the most innovative trend since Hadoop showed up over 5 years ago. Below is a guide on how to find the right GPU Database for you. What is a GPU database? To put it simply, a GPU database is a database, relational or non-relational, that uses a GPU (graphical processing unit) to perform some database operations. For example, GPU databases are typically fast. Subsequently, GPU databases are more flexible in processing many different types of data, or much larger amounts of data. Most of the GPU databases tend to focus on analytics, and they’re offering it to a market that was oversold on Hadoop for Big Data analytics. The truth is Hadoop was not designed for relational data analytics, and yet the widespread use of Hadoop led to the creation of many projects that aimed to do exactly that, with mixed results. Hadoop is now past it’s hype cycle and is now entering the “Plateau of productivity” stage. Making the most of GPUs GPUs and CPUs can cooperate quite nicely, when both are taken advantage for their unique benefits, but this is not just for performance’s sake. In reality, the different architecture of the GPU makes it unsuitable for performing specific operations, like text manipulations that may differ based on the content. Because the (thousands of) GPU cores like to work the same way, having contents that will cause the code to behave differently will cause a significant drop in performance — essentially becoming partially sequential and not strictly parallel (in what Nvidia’s CUDA calls branch divergence). A GPU excels at performing (relatively simple), repetitive operations on large amounts of data in many streams. It just wouldn’t function very well if the branches diverge, the operations launch too few streams (kernels), or the operations can’t be parallelized. One of the reasons Nvidia GPUs are so popular among developers is that packages like Thrust, CUB and Modern GPU have made writing performant, parallel GPU code quite easy, in contrast with optimized, vectorized, efficiently threaded CPU code. GPUs for Analytics In general, GPUs can be used for a variety of different stages in the analytics pipeline. It can be used as the main database, as part of the processing pipeline, or just for the resulting analytic dataset — for example, with popular frameworks like TensorFlow. Let’s take a look at two of the main areas where GPUs can help in the analytics pipeline. GPUs for Stream Processing New stream processing solutions, like FASTDATA.io’s Plasma Engine can take advantage of GPUs for stream processing data coming in and out of databases (GPU or not). This tool can be used to perform analysis and/or transformation of streaming data on the GPU. The main competitor to FASTDATA’s engine is GPU-enabled Spark, which is available as an open-source add-on from IBM. GPU Databases for Analytics With the exception of PG-Strom and Brytlyt, which retrofits the open-source Postgres RDBMS by augmenting it with GPU-aware parts, all other GPU databases are purpose built for analytics. Blazegraph is another exception, because it is designed for graph operations. This leaves us with four players, dealing mostly with relational, structured analytics, with an SQL interface. This chart below should help you understand which of these GPU database is right for you: GPU Databases for Analytics SQream DB (formerly MapD) Omnisci Kinetica BlazingDB (formerly PG-Strom) HeteroDB Blazegraph GPUs for Stream Processing FASTDATA.io IBM Spark GPU

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