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Beware of NVIDIA Graphic cards in Laptops

NVIDIA famously known for its high end graphics cards which makes everyone to own a machine with NVIDIA graphics card. But beware of NVIDIA graphics card as they suffer OVER HEATING issue because of weak die/packaging material. It seems NVIDIA G84 & G86 graphic chipsets had this problem.
According to our sources, the failures are caused by a solder bump that connects the I/O termination of the silicon chip to the pad on the substrate. In Nvidia’s GPUs, this solder bump is created using high-lead. A thermal mismatch between the chip and the substrate has substantially grown in recent chip generations, apparently leading to fatigue cracking. Add into the equation a growing chip size (double the chip dimension, quadruple the stress on the bump) as well as generally hotter chips and you may have the perfect storm to take high lead beyond its limits. Apparently, problems arise at what Nvidia claims to be "extreme temperatures" and what we hear may be temperatures not too much above 70 degrees Celsius.

What supports the theory that a high-lead solder bump in fact is at fault is the fact that Nvidia ordered an immediate switch to use eutectic solders instead of high-lead versions in the last week of July.
Major laptop manufacturers like DELL, HP has accepted that there are some issues with laptops shipped with a NVIDIA graphics cards.

According to NVIDIA
, these affected GPUs are experiencing higher than expected failure rates causing video problems and it is because of weak die/packaging material set, which may fail with higher GPU temperature fluctuations.

If your NVIDIA GPU fails, you may see intermittent symptoms during early stages of failure that include:

* Multiple images
* Random characters on the screen
* Lines on the screen
* No video
* Black Screen

DELL agreed that some of its laptop models namely Inspiron 1420, Latitude D630, Latitude D630c, Dell Precision M2300, Vostro Notebook 1310, Vostro Notebook 1400, Vostro Notebook 1510, Vostro Notebook 1710, XPS M1330, XPS M1530 are facing the issue and released a BIOS upgradation to minimize the effect of the GPU failure problem and new systems are being shipped with the updated BIOS revisions.
DELL is offering an additional 12-month limited warranty enhancement specific to this GPU failure issue. For all customers worldwide, DELL plans to add 12 months of coverage for this issue to the existing limited warranty up to 60 months from the date of purchase for the given list of systems.

DELL's BIOS fix is simply turning on the laptop fan much more than usual as a result laptop battery is drained. The 'fix' keeps the fan on much more and destroys battery life.

HP also identified a hardware issue with certain HP Pavilion dv2000/dv6000/dv9000 and Compaq Presario V3000/V6000 series notebook PCs, and has also released a new BIOS for these notebook PCs.
HP says"This service enhancement program is available in North America for 24 months after the start of your original standard limited warranty for issues listed below; otherwise your current standard limited warranty applies. Customers who already have a 24 month or longer warranty period will be covered under their existing standard HP Limited Warranty."
Apple is also facing the same problem with the macbook pro's.
Apple says, NVIDIA assured Apple that Mac computers with these graphics processors were not affected. However, after an Apple-led investigation, Apple has determined that some MacBook Pro computers with the NVIDIA GeForce 8600M GT graphics processor may be affected. If the NVIDIA graphics processor in your MacBook Pro has failed, or fails within two years of the original date of purchase, a repair will be done free of charge, even if your MacBook Pro is out of warranty.
Customers are really angry as the OEM's are not providing a permanent solution to the GPU problem and also not including some of the laptop models that are facing the same issue. Even some of the customers sued NVIDIA to repair their laptops with faulty graphics cards.

We can see still DELL is shipping laptops with defective NVIDIA graphics card.


Cache Memory - Part2

Interaction Policies with Main Memory
Basically READ operations dominate processor cache accesses since many of the instruction accesses are READ operation’s and most instructions do not WRITE into memory. When the address of the block to be READ is available then the tag is read and if it is a HIT then READ from it.

In case of a miss the READ policies are:
  • Read Through - Reading a block directly from main memory.
  • No Read Through - Reading a block from main memory into cache and then from cache to CPU. So we even update the cache memory.

Basically a Miss is comparatively slow because they require the data to be transferred from main memory to CPU which incurs a delay since main memory is much slower than cache memory, and also incurs the overhead for recording the new data in the cache before it is delivered to the processor. To take advantage of Locality of Reference, the CPU copies data into the cache whenever it accesses an address not present in the cache. Since it is likely the system will access that same location shortly, the system will save wait states by having that data in the cache. Thus cache memory handles the temporal aspects of memory access, but not the spatial aspects.

Accessing Caching memory locations won't speed up the program execution if we constantly access consecutive memory locations (Spatial Locality of Reference). To solve this problem, most caching systems read several consecutive bytes from memory when a cache miss occurs. 80x86 CPUs, for example, read between 16 and 64 bytes at a shot (depending upon the CPU) upon a cache miss. If you read 16 bytes, why read them in blocks rather than as you need them? As it turns out, most memory chips available today have special modes which let you quickly access several consecutive memory locations on the chip. The cache exploits this capability to reduce the average number of wait states needed to access memory.
Cache READ operation
Cache READ operation

It is not the same with Cache WRITE operation. Modifying a block cannot begin until the tag is checked to see if the address is a hit. Also the processor specifies the size of the write, usually between 1 and 8 bytes; only that portion of the block can be changed. In contrast, reads can access more bytes than necessary without a problem.

The Cache WRITE policies on write hit often distinguish cache designs:
  • Write Through - the modified data is written back to both the block in the cache memory and in the main memory.

    1. READ miss never results in writes to main memory.
    2. Easy to implement
    3. Main Memory always has the most current copy of the data (consistent)

    1. WRITE operation is slower as we have to update both Main Memory and Cache Memory.
    2. Every write needs a main memory access as a result uses more memory bandwidth
Write Through in cache
  • Write Back - the modified data is first written only to the block in the cache memory. The modified cache block is written to main memory only when it is replaced. In order to reduce the frequency of writing back blocks on replacement, a dirty bit (a status bit) is commonly used to indicate whether the block is dirty (modified while in the cache) or clean (not modified). If it is clean the block is not written on a miss.

    1. WRITE’s occur at the speed of the cache memory.
    2. Multiple WRITE’s within a block require only one WRITE to main memory as a result uses less memory bandwidth

    1. Harder to implement
    2. Main Memory is not always consistent with cache reads that result in replacement may cause writes of dirty blocks to main memory.
Incase of Cache Write MISS we have to options.
  • Write Allocate - the memory block is first loaded into cache memory from main memory on a write miss, followed by the write-hit action.
  • No Write Allocate - the block is directly modified in the main memory and not loaded into the cache memory.
Although either write-miss policy could be used with write through or write back, write-back caches generally use write allocate (hoping that subsequent writes to that block will be captured by the cache) and write-through caches often use no-write allocate (since subsequent writes to that block will still have to go to memory).

The data in main memory being cached may be changed by other entities, in which case the copy in the cache may become out-of-date or stale. Alternatively, when the CPU updates the data in the cache, copies of data in other caches will become stale. Communication protocols between the cache managers which keep the data consistent are known as cache coherence protocols.


Principle of Locality

When a program executes on a computer, most of the memory references are not made uniformly to a small number of locations. Here the Locality of the reference does matter.

Locality of Reference, also known as the Principle of Locality, the phenomenon of the same value or related storage locations being frequently accessed. Locality occurs in time(temporal locality) and in space (spatial locality).
  • Temporal Locality refers to the reuse of specific data and/or resources within relatively small time durations.
  • Spatial Locality refers to the use of data elements within relatively close storage locations. Sequential locality, a special case of spatial locality, occurs when data elements are arranged and accessed linearly, eg, traversing the elements in a one-dimensional array.
To be very simple when exhibiting spatial locality, a program accesses consecutive memory locations and during temporal locality of reference a program repeatedly accesses the same memory location during a short time period. Both forms of locality occur in the following Pascal code segment:
  for i := 0 to 10 do
A [i] := 0;

In the above Pascal code, the variable 'i' is referenced several times in for loop where 'i' is compared against 10 to see if the loop is complete and also incremented by one at the end of the loop. This shows temporal locality of reference in action since the CPU accesses 'i' at different points in a short time period.

This program also exhibits spatial locality of reference. The loop itself zeros out the elements of array A by writing a zero to the first location in A, then to the second location in A, and so on. Assume Pascal stores elements of A into consecutive memory locations then on each loop iteration it accesses adjacent memory locations.

Cache Memory - Set Associative Mapped Cache

Set Associative mapping scheme combines the simplicity of Direct mapping with the flexibility of Fully Associative mapping. It is more practical than Fully Associative mapping because the associative portion is limited to just a few slots that make up a set.

In this mapping mechanism, the cache memory is divided into 'v' sets, each consisting of 'n' cache lines. A block from Main memory is first mapped onto a specific cache set, and then it can be placed anywhere within that set. This type of mapping has very efficient ratio between implementation and efficiency. The set is usually chosen by

Cache set number = (Main memory block number) MOD (Number of sets in the cache memory)

If there are 'n' cache lines in a set, the cache placement is called n-way set associative i.e. if there are two blocks or cache lines per set, then it is a 2-way set associative cache mapping and four blocks or cache lines per set, then it is a 4-way set associative cache mapping.

Let us assume we have a Main Memory of size 4GB (232), with each byte directly addressable by a 32-bit address. We will divide Main memory into blocks of each 32 bytes (25). Thus there are 128M (i.e. 232/25 = 227) blocks in Main memory.

We have a Cache memory of 512KB (i.e. 219), divided into blocks of each 32 bytes (25). Thus there are 16K (i.e. 219/25 = 214) blocks also known as Cache slots or Cache lines in cache memory. It is clear from above numbers that there are more Main memory blocks than Cache slots.

NOTE: The Main memory is not physically partitioned in the given way, but this is the view of Main memory that the cache sees.

NOTE: We are dividing both Main Memory and cache memory into blocks of same size i.e. 32 bytes.

Let us try 2-way set associative cache mapping i.e. 2 cache lines per set. We will divide 16K cache lines into sets of 2 and hence there are 8K (214/2 = 213) sets in the Cache memory.

Cache Size = (Number of Sets) * (Size of each set) * (Cache line size)

So even using the above formula we can find out number of sets in the Cache memory i.e.

219 = (Number of Sets) * 2 * 25

Number of Sets = 219 / (2 * 25) = 213.

Set Associative Mapped CacheWhen an address is mapped to a set, the direct mapping scheme is used, and then associative mapping is used within a set.

The format for an address has 13 bits in the set field, which identifies the set in which the addressed word will be found if it is in the cache. There are five bits for the word field as before and there is 14-bit tag field that together make up the remaining 32 bits of the address as shown below:
Set Associative Mapped CacheAs an example of how the set associative cache views a Main memory address, consider again the address (A035F014)16. The leftmost 14 bits form the tag field, followed by 13 bits for the set field, followed by five bits for the word field as shown below:
cache memory - Set Associative mapped
In the below example we have chosen the block 14 from Main memory and compared it with the different block replacement algorithms. In Direct Mapped cache it can be placed in Frame 6 since 14 mod 8 = 6. In Set associative cache it can be placed in set 2.
cache memory - Set Associative mappedCheckout one more solved problem below.
cache memory - Set Associative mapped

1. Computer Architecture Tutorial - By Gurpur M. Prabhu.

2. Computer Architecture And Organization: An Integrated Approach - By Murdocca & Vincent Heuring

Cache Memory - Fully Associative Mapped Cache

If a Main memory block can be placed in any of the Cache slots, then the cache is said to be mapped in fully associative.

Let us assume we have a Main Memory of size 4GB (232), with each byte directly addressable by a 32-bit address. We will divide Main memory into blocks of each 32 bytes (25). Thus there are 128M (i.e. 232/25 = 227) blocks in Main memory.

We have a Cache memory of 512KB (i.e. 219), divided into blocks of each 32 bytes (25). Thus there are 16K (i.e. 219/25 = 214) blocks also known as Cache slots or Cache lines in cache memory. It is clear from above numbers that there are more Main memory blocks than Cache slots.

NOTE: The Main memory is not physically partitioned in the given way, but this is the view of Main memory that the cache sees.

NOTE: We are dividing both Main Memory and cache memory into blocks of same size i.e. 32 bytes.

In fully associative mapping any one of the 128M (i.e. 227) Main memory blocks can be mapped into any of the single Cache slot. To keep track of which one of the 227 possible blocks is in each slot, a 27-bit tag field is added to each slot which holds an identifier in the range from 0 to 227 – 1. The tag field is the most significant 27 bits of the 32-bit memory address presented to the cache.
Fully Associative Mapped Cache
In an associative mapped cache, each Main memory block can be mapped to any slot. The mapping from main memory blocks to cache slots is performed by partitioning an address into fields for the tag and the word (also known as the “byte” field) as shown below:
Fully Associative Mapped Cache
When a reference is made to a Main memory address, the cache hardware intercepts the reference and searches the cache tag memory to see if the requested block is in the cache. For each slot, if the valid bit is 1, then the tag field of the referenced address is compared with the tag field of the slot. All of the tags are searched in parallel, using an associative memory. If any tag in the cache tag memory matches the tag field of the memory reference, then the word is taken from the position in the slot specified by the word field. If the referenced word is not found in the cache, then the main memory block that contains the word is brought into the cache and the referenced word is then taken from the cache. The tag, valid, and dirty fields are updated, and the program resumes execution.

Associative mapped cache has the advantage of placing any main memory block into any cache line. This means that regardless of how irregular the data and program references are, if a slot is available for the block, it can be stored in the cache. This results in considerable hardware overhead needed for cache bookkeeping.

Although this mapping scheme is powerful enough to satisfy a wide range of memory access situations, there are two implementation problems that limit performance.
  • The process of deciding which slot should be freed when a new block is brought into the cache can be complex. This process requires a significant amount of hardware and introduces delays in memory accesses.
  • When the cache is searched, the tag field of the referenced address must be compared with all 214 tag fields in the cache.