Pi Calculated to 105 Trillion Digits. (Stored on 1 Petabyte of SSDs)

Pi was calculated to 100 trillion decimal places in 2022 by a Google team lead by cloud developer advocate Emma Haruka Iwao.

But 2024’s “pi day” saw a new announcement…

After successfully breaking the speed record for calculating pi to 100 trillion digits last year, the team at StorageReview has taken it up a notch, revealing all the numbers of Pi up to 105 trillion digits! Spoiler: the 105 trillionth digit of Pi is 6!

Owner and Editor-in-Chief Brian Beeler led the team that used 36 Solidigm SSDs (nearly a petabyte) for their unprecedented capacity and reliability required to store the calculated digits of Pi. Although there is no practical application for this many digits, the exercise underscores the astounding capabilities of modern hardware and an achievement in computational and storage technology…

For an undertaking of this size, which took 75 days, the role of storage cannot be understated. “For the Pi computation, we’re entirely restricted by storage, says Beeler. “Faster CPUs will help accelerate the math, but the limiting factor to many new world records is the amount of local storage in the box. For this run, we’re again leveraging Solidigm D5-P5316 30.72TB SSDs to help us get a little over 1P flash in the system.

“These SSDs are the only reason we could break through the prior records and hit 105 trillion Pi digits.”

“Leveraging a combination of open-source and proprietary software, the team at StorageReview optimized the algorithmic process to fully exploit the hardware’s capabilities, reducing computational time and enhancing efficiency,” Beeler says in the announcement.

There’s a video on YouTubewhere the team discusses their effort.

Read more of this story at Slashdot.

Math Scores Fell In Nearly Every State, Reading Dipped On National Exam

U.S. students in most states and across almost all demographic groups have experienced troubling setbacks in both math and reading, according to an authoritative national exam released on Monday, offering the most definitive indictment yet of the pandemic’s impact on millions of schoolchildren. The New York Times reports: In math, the results were especially devastating, representing the steepest declines ever recorded on the National Assessment of Educational Progress, known as the nation’s report card, which tests a broad sampling of fourth and eighth graders and dates to the early 1990s. In the test’s first results since the pandemic began, math scores for eighth graders fell in nearly every state. A meager 26 percent of eighth graders were proficient, down from 34 percent in 2019. Fourth graders fared only slightly better, with declines in 41 states. Just 36 percent of fourth graders were proficient in math, down from 41 percent.

Reading scores also declined in more than half the states, continuing a downward trend that had begun even before the pandemic. No state showed sizable improvement in reading. And only about one in three students met proficiency standards, a designation that means students have demonstrated competency and are on track for future success. And for the country’s most vulnerable students, the pandemic has left them even further behind. The drops in their test scores were often more pronounced, and their climbs to proficiency are now that much more daunting.

Read more of this story at Slashdot.

DeepMind Breaks 50-Year Math Record Using AI; New Record Falls a Week Later

Last week, DeepMind announced it discovered a more efficient way to perform matrix multiplication, conquering a 50-year-old record. This week, two Austrian researchers at Johannes Kepler University Linz claim they have bested that new record by one step. Ars Technica reports: In 1969, a German mathematician named Volker Strassen discovered the previous-best algorithm for multiplying 4×4 matrices, which reduces the number of steps necessary to perform a matrix calculation. For example, multiplying two 4×4 matrices together using a traditional schoolroom method would take 64 multiplications, while Strassen’s algorithm can perform the same feat in 49 multiplications. Using a neural network called AlphaTensor, DeepMind discovered a way to reduce that count to 47 multiplications, and its researchers published a paper about the achievement in Nature last week.

To discover more efficient matrix math algorithms, DeepMind set up the problem like a single-player game. The company wrote about the process in more detail in a blog post last week. DeepMind then trained AlphaTensor using reinforcement learning to play this fictional math game — similar to how AlphaGo learned to play Go — and it gradually improved over time. Eventually, it rediscovered Strassen’s work and those of other human mathematicians, then it surpassed them, according to DeepMind. In a more complicated example, AlphaTensor discovered a new way to perform 5×5 matrix multiplication in 96 steps (versus 98 for the older method).

This week, Manuel Kauers and Jakob Moosbauer of Johannes Kepler University in Linz, Austria, published a paper claiming they have reduced that count by one, down to 95 multiplications. It’s no coincidence that this apparently record-breaking new algorithm came so quickly because it built off of DeepMind’s work. In their paper, Kauers and Moosbauer write, “This solution was obtained from the scheme of [DeepMind’s researchers] by applying a sequence of transformations leading to a scheme from which one multiplication could be eliminated.”

Read more of this story at Slashdot.

China Punishes 27 People Over ‘Tragically Ugly’ Illustrations In Maths Textbook

Chinese authorities have punished 27 people over the publication of a maths textbook that went viral over its “tragically ugly” illustrations. The Guardian reports: A months-long investigation by a ministry of education working group found the books were “not beautiful,” and some illustrations were “quite ugly” and did not “properly reflect the sunny image of China’s children.” The mathematics books were published by the People’s Education Press almost 10 years ago, and were reportedly used in elementary schools across the country. But they went viral in May after a teacher published photos of the illustrations inside, including people with distorted faces and bulging pants, boys pictures grabbing girls’ skirts and at least one child with an apparent leg tattoo.

Social media users were largely amused by the illustrations, but many also criticized them as bringing disrepute and “cultural annihilation” to China, speculating they were the deliberate work of western infiltrators in the education sector. Related hashtags were viewed billions of times, embarrassing the Communist party and education authorities who announced a review of all textbooks “to ensure that the textbooks adhere to the correct political direction and value orientation.”

In a lengthy statement released on Monday, the education authorities said 27 individuals were found to have “neglected their duties and responsibilities” and were punished, including the president of the publishing house, who was given formal demerits, which can affect a party member’s standing and future employment. The editor-in-chief and the head of the maths department editing office were also given demerits and dismissed from their roles. The statement said the illustrators and designers were “dealt with accordingly” but did not give details. They and their studios would no longer be engaged to work on textbook design or related work, it said. The highly critical statement found a litany of issues with the books, including critiquing the size, quantity and quality of illustrations, some of which had “scientific and normative problems.”

Read more of this story at Slashdot.

Linux Random Number Generator Sees Major Improvements

An anonymous Slashdot reader summarizes some important news from the web page of Jason Donenfeld (creator of the open-source VPN protocol WireGuard):

The Linux kernel’s random number generator has seen its first set of major improvements in over a decade, improving everything from the cryptography to the interface used. Not only does it finally retire SHA-1 in favor of BLAKE2s [in Linux kernel 5.17], but it also at long last unites ‘/dev/random’ and ‘/dev/urandom’ [in the upcoming Linux kernel 5.18], finally ending years of Slashdot banter and debate:

The most significant outward-facing change is that /dev/random and /dev/urandom are now exactly the same thing, with no differences between them at all, thanks to their unification in random: block in /dev/urandom. This removes a significant age-old crypto footgun, already accomplished by other operating systems eons ago. […] The upshot is that every Internet message board disagreement on /dev/random versus /dev/urandom has now been resolved by making everybody simultaneously right! Now, for the first time, these are both the right choice to make, in addition to getrandom(0); they all return the same bytes with the same semantics. There are only right choices.

Phoronix adds:
One exciting change to also note is the getrandom() system call may be a hell of a lot faster with the new kernel. The getrandom() call for obtaining random bytes is yielding much faster performance with the latest code in development. Intel’s kernel test robot is seeing an 8450% improvement with the stress-ng getrandom() benchmark. Yes, an 8450% improvement.

Read more of this story at Slashdot.